"""simple docstring"""





import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    XLMForMultipleChoice,
			    XLMForQuestionAnswering,
			    XLMForQuestionAnsweringSimple,
			    XLMForSequenceClassification,
			    XLMForTokenClassification,
			    XLMModel,
			    XLMWithLMHeadModel,
			)
			from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Any      ,    _A							:							Optional[Any]      ,    _A							:							Tuple=1_3      ,    _A							:							Optional[Any]=7      ,    _A							:							Dict=True      ,    _A							:							Optional[Any]=True      ,    _A							:							str=True      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=True      ,    _A							:							Tuple=False      ,    _A							:							Optional[int]=False      ,    _A							:							str=False      ,    _A							:							int=2      ,    _A							:							Union[str, Any]=9_9      ,    _A							:							int=0      ,    _A							:							Dict=3_2      ,    _A							:							List[str]=5      ,    _A							:							Any=4      ,    _A							:							str=0.1      ,    _A							:							Any=0.1      ,    _A							:							int=5_1_2      ,    _A							:							List[Any]=2      ,    _A							:							Union[str, Any]=0.02      ,    _A							:							Optional[Any]=2      ,    _A							:							List[str]=4      ,    _A							:							Optional[int]="last"      ,    _A							:							str=True      ,    _A							:							List[str]=None      ,    _A							:							List[Any]=0      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    parent
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    seq_length
											_SCREAMING_SNAKE_CASE		:						int			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_input_lengths
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    use_token_type_ids
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    gelu_activation
											_SCREAMING_SNAKE_CASE		:						str			  =    sinusoidal_embeddings
											_SCREAMING_SNAKE_CASE		:						Dict			  =    causal
											_SCREAMING_SNAKE_CASE		:						Dict			  =    asm
											_SCREAMING_SNAKE_CASE		:						str			  =    n_langs
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Dict			  =    n_special
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						str			  =    num_labels
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_choices
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    summary_type
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    use_proj
											_SCREAMING_SNAKE_CASE		:						Dict			  =    scope
											_SCREAMING_SNAKE_CASE		:						str			  =    bos_token_id





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    random_attention_mask([self.batch_size, self.seq_length])

											_SCREAMING_SNAKE_CASE		:						str			  =    None
											if self.use_input_lengths:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (
																		    ids_tensor([self.batch_size]      ,    vocab_size=2) + self.seq_length - 2
																		)  # small variation of seq_length

											_SCREAMING_SNAKE_CASE		:						Any			  =    None
											if self.use_token_type_ids:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.n_langs)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    None
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None
											if self.use_labels:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    ids_tensor([self.batch_size]      ,    self.type_sequence_label_size)
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ids_tensor([self.batch_size]      ,    2).float()
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    ids_tensor([self.batch_size]      ,    self.num_choices)

											_SCREAMING_SNAKE_CASE		:						int			  =    self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											return XLMConfig(
											    vocab_size=self.vocab_size      ,    n_special=self.n_special      ,    emb_dim=self.hidden_size      ,    n_layers=self.num_hidden_layers      ,    n_heads=self.num_attention_heads      ,    dropout=self.hidden_dropout_prob      ,    attention_dropout=self.attention_probs_dropout_prob      ,    gelu_activation=self.gelu_activation      ,    sinusoidal_embeddings=self.sinusoidal_embeddings      ,    asm=self.asm      ,    causal=self.causal      ,    n_langs=self.n_langs      ,    max_position_embeddings=self.max_position_embeddings      ,    initializer_range=self.initializer_range      ,    summary_type=self.summary_type      ,    use_proj=self.use_proj      ,    num_labels=self.num_labels      ,    bos_token_id=self.bos_token_id      ,    )





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[Any]      ,    _A							:							List[str]      ,    _A							:							int      ,    _A							:							Dict      ,    _A							:							Optional[Any]      ,    _A							:							Optional[Any]      ,    _A							:							Any      ,    _A							:							List[str]      ,    _A							:							Optional[int]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    XLMModel(config=A_)
											model.to(A_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(A_      ,    lengths=A_      ,    langs=A_)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model(A_      ,    langs=A_)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model(A_)
											self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size))





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							str      ,    _A							:							Union[str, Any]      ,    _A							:							Any      ,    _A							:							Any      ,    _A							:							Any      ,    _A							:							Union[str, Any]      ,    _A							:							List[str]      ,    _A							:							List[str]      ,    _A							:							List[str]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    XLMWithLMHeadModel(A_)
											model.to(A_)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						Any			  =    model(A_      ,    token_type_ids=A_      ,    labels=A_)
											self.parent.assertEqual(result.loss.shape      ,    ())
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size))





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							Tuple      ,    _A							:							str      ,    _A							:							int      ,    _A							:							str      ,    _A							:							Optional[Any]      ,    _A							:							Any      ,    _A							:							Any      ,    _A							:							Dict      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    XLMForQuestionAnsweringSimple(A_)
											model.to(A_)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						str			  =    model(A_)

											_SCREAMING_SNAKE_CASE		:						int			  =    model(A_      ,    start_positions=A_      ,    end_positions=A_)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    outputs
											self.parent.assertEqual(result.start_logits.shape      ,    (self.batch_size, self.seq_length))
											self.parent.assertEqual(result.end_logits.shape      ,    (self.batch_size, self.seq_length))





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Optional[int]      ,    _A							:							Dict      ,    _A							:							Optional[Any]      ,    _A							:							List[Any]      ,    _A							:							List[str]      ,    _A							:							List[Any]      ,    _A							:							Optional[Any]      ,    _A							:							str      ,    _A							:							Any      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    XLMForQuestionAnswering(A_)
											model.to(A_)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(A_)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(
											    A_      ,    start_positions=A_      ,    end_positions=A_      ,    cls_index=A_      ,    is_impossible=A_      ,    p_mask=A_      ,    )

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(
											    A_      ,    start_positions=A_      ,    end_positions=A_      ,    cls_index=A_      ,    is_impossible=A_      ,    )

											((_SCREAMING_SNAKE_CASE      )  ,				      )		:						Optional[int]			  =    result_with_labels.to_tuple()

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(A_      ,    start_positions=A_      ,    end_positions=A_)

											((_SCREAMING_SNAKE_CASE      )  ,				      )		:						Optional[int]			  =    result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape      ,    ())
											self.parent.assertEqual(result.start_top_log_probs.shape      ,    (self.batch_size, model.config.start_n_top))
											self.parent.assertEqual(result.start_top_index.shape      ,    (self.batch_size, model.config.start_n_top))
											self.parent.assertEqual(
											    result.end_top_log_probs.shape      ,    (self.batch_size, model.config.start_n_top * model.config.end_n_top))
											self.parent.assertEqual(
											    result.end_top_index.shape      ,    (self.batch_size, model.config.start_n_top * model.config.end_n_top))
											self.parent.assertEqual(result.cls_logits.shape      ,    (self.batch_size,))





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							int      ,    _A							:							Union[str, Any]      ,    _A							:							List[Any]      ,    _A							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							Union[str, Any]      ,    _A							:							str      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    XLMForSequenceClassification(A_)
											model.to(A_)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(A_)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(A_      ,    labels=A_)
											self.parent.assertEqual(result.loss.shape      ,    ())
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size))





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[Any]      ,    _A							:							str      ,    _A							:							Tuple      ,    _A							:							List[str]      ,    _A							:							Dict      ,    _A							:							Dict      ,    _A							:							Union[str, Any]      ,    _A							:							Dict      ,    _A							:							Any      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    XLMForTokenClassification(A_)
											model.to(A_)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(A_      ,    attention_mask=A_      ,    labels=A_)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							int      ,    _A							:							str      ,    _A							:							int      ,    _A							:							Tuple      ,    _A							:							List[Any]      ,    _A							:							List[str]      ,    _A							:							List[str]      ,    _A							:							Optional[Any]      ,    _A							:							int      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.num_choices
											_SCREAMING_SNAKE_CASE		:						str			  =    XLMForMultipleChoice(config=A_)
											model.to(A_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    input_ids.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						Any			  =    token_type_ids.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    input_mask.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						Any			  =    model(
											    A_      ,    attention_mask=A_      ,    token_type_ids=A_      ,    labels=A_      ,    )
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices))





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				(
											    _SCREAMING_SNAKE_CASE      
											)  ,				      
											)		:						Union[str, Any]			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
											return config, inputs_dict




@require_torch
class  _snake_case		(    _lowercase   ,       _lowercase   ,       _lowercase   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				a			=						(
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				a			=						(
				    {
				        '''feature-extraction''': XLMModel,
				        '''fill-mask''': XLMWithLMHeadModel,
				        '''question-answering''': XLMForQuestionAnsweringSimple,
				        '''text-classification''': XLMForSequenceClassification,
				        '''text-generation''': XLMWithLMHeadModel,
				        '''token-classification''': XLMForTokenClassification,
				        '''zero-shot''': XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Optional[int]      ,    _A							:							Optional[int]      ,    _A							:							Tuple      ,    _A							:							Any      ,    _A							:							str):



											"""simple docstring"""







											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith("""Fast""")
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[int]      ,    _A							:							Tuple      ,    _A							:							str=False):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    super()._prepare_for_class(A_      ,    A_      ,    return_labels=A_)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.zeros(
																									    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=A_)
																									_SCREAMING_SNAKE_CASE		:						Any			  =    torch.zeros(
																									    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=A_)

											return inputs_dict





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    XLMModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Any			  =    ConfigTester(self      ,    config_class=A_      ,    emb_dim=3_7)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*A_)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*A_)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*A_)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*A_)





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*A_)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*A_)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*A_)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Dict      ,    _A							:							List[Any]      ,    _A							:							List[Any]      ,    _A							:							Dict      ,    _A							:							Union[str, Any]      ,    _A							:							List[Any]=False      ,    _A							:							Tuple=1):



											"""simple docstring"""







											self.assertIsInstance(A_      ,    A_)
											self.assertListEqual(
											    [isinstance(A_      ,    A_) for iter_attentions in attentions]      ,    [True] * len(A_))
											self.assertEqual(len(A_)      ,    (max_length - min_length) * num_beam_groups)

											for idx, iter_attentions in enumerate(A_):
																		# adds PAD dummy token
																		_SCREAMING_SNAKE_CASE		:						str			  =    min_length + idx + 1
																		_SCREAMING_SNAKE_CASE		:						int			  =    min_length + idx + 1

																		_SCREAMING_SNAKE_CASE		:						str			  =    (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]      ,    [expected_shape] * len(A_))





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Optional[Any]      ,    _A							:							List[str]      ,    _A							:							Dict      ,    _A							:							Optional[Any]      ,    _A							:							Dict      ,    _A							:							Tuple=False      ,    _A							:							Optional[Any]=1):



											"""simple docstring"""







											self.assertIsInstance(A_      ,    A_)
											self.assertListEqual(
											    [isinstance(A_      ,    A_) for iter_hidden_states in hidden_states]      ,    [True] * len(A_)      ,    )
											self.assertEqual(len(A_)      ,    (max_length - min_length) * num_beam_groups)

											for idx, iter_hidden_states in enumerate(A_):
																		# adds PAD dummy token
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    min_length + idx + 1
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]      ,    [expected_shape] * len(A_)      ,    )
											pass





				@slow
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    XLMModel.from_pretrained(A_)
																		self.assertIsNotNone(A_)




@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""")
											model.to(A_)
											_SCREAMING_SNAKE_CASE		:						int			  =    torch.tensor([[1_4, 4_4_7]]      ,    dtype=torch.long      ,    device=A_)  # the president
											_SCREAMING_SNAKE_CASE		:						Dict			  =    [
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											    1_4,
											    4_4_7,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model.generate(A_      ,    do_sample=A_)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()      ,    A_)
"""simple docstring"""





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=1_026     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"     ,	__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"     ,	)->    Union[str, Any]:
							set_seed(3  )
							# generate train_data and objective_set
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    generate_datasets(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	number=__SCREAMING_SNAKE_CASE     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE  )
							# keeps model same across runs
							set_seed(4  )
							# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
							# can we train on GPU?
							_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )

							# load pretrained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    load_gpta("""gpt2"""  ).to(__SCREAMING_SNAKE_CASE  )
							print("""computing perplexity on objective set"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).item()
							print("""perplexity on objective set:"""     ,	__SCREAMING_SNAKE_CASE  )

							# collect igf pairs and save to file demo.jbl
							collect_objective_set(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# clean up, delete model and data we don't need anymore
							del model, train_data, objective_set
							torch.cuda.empty_cache()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=15     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE="igf_model.pt"     ,	)->    Optional[int]:
							set_seed(42  )

							# Load pre-trained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )

							# Initialize secondary learner to use embedding weights of model
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    SecondaryLearner(__SCREAMING_SNAKE_CASE  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						Any			  =    train_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	max_epochs=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	eval_freq=100     ,	igf_model_path=__SCREAMING_SNAKE_CASE     ,	)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=1_000     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=recopy_gpta     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"     ,	)->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RandomSampler(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(__SCREAMING_SNAKE_CASE     ,	sampler=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    max_steps // (len(__SCREAMING_SNAKE_CASE  )) + 1
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    0
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.zeros((1, context_len)     ,	dtype=torch.long     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    recopy_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(__SCREAMING_SNAKE_CASE  )
														secondary_learner.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						int			  =    []

							# Compute the performance of the transformer model at the beginning
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							test_perps.append(__SCREAMING_SNAKE_CASE  )
							print("""Test perplexity, step"""     ,	__SCREAMING_SNAKE_CASE     ,	""":"""     ,	__SCREAMING_SNAKE_CASE  )
							for epoch in range(int(__SCREAMING_SNAKE_CASE  )  ):
														for step, example in enumerate(__SCREAMING_SNAKE_CASE  ):
																					torch.cuda.empty_cache()
																					_SCREAMING_SNAKE_CASE		:						Any			  =    random.randint(0     ,	example.size(2  ) - context_len - 1  )
																					_SCREAMING_SNAKE_CASE		:						int			  =    example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    True

																					if secondary_learner is not None:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    secondary_learner.forward(
																												    torch.tensor(__SCREAMING_SNAKE_CASE     ,	dtype=torch.long     ,	device=__SCREAMING_SNAKE_CASE  ).unsqueeze(0  )  )[0].item()
																												observed_qs.append(float(__SCREAMING_SNAKE_CASE  )  )

																												# Here we implement the simple non-constant threshold for the predicted IG(X) value
																												# We will decay the selectivity of our secondary learner filter from
																												# 1 standard deviation above average to 1 below average after 10 batches.

																												if global_step == 10:
																																			_SCREAMING_SNAKE_CASE		:						Dict			  =    -1
																												if predicted_q < threshold:
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()  )  )
																												_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    outputs[0]
																												lm_loss.backward()
																												examples += 1

																					del outputs

																					# Once the batch is filled with enough contexts, backprop on the batch.
																					if examples == batch_size:
																												torch.cuda.empty_cache()
																												_SCREAMING_SNAKE_CASE		:						Any			  =    0
																												# Do LM backprop
																												torch.nn.utils.clip_grad_norm_(model.parameters()     ,	3.0  )
																												lm_optimizer.step()
																												lm_scheduler.step()  # Update learning rate schedule
																												global_step += 1
																												# Compute the performance of the transformer model at this batch
																												if global_step % eval_interval == 0:
																																			_SCREAMING_SNAKE_CASE		:						Tuple			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			test_perps.append(__SCREAMING_SNAKE_CASE  )

																																			print("""Test perplexity, step"""     ,	__SCREAMING_SNAKE_CASE     ,	""":"""     ,	__SCREAMING_SNAKE_CASE  )
            # Break out of the loop after 60 batches
																					if max_steps > 0 and global_step > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					break

    # save finetuned transformer model
							torch.save(model.state_dict()     ,	__SCREAMING_SNAKE_CASE  )
							torch.cuda.empty_cache()
							# Do some cleaning up so we can reinitialize for the next run of this function
							del lm_optimizer
							del lm_scheduler
							return model



def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""  )

							# Required parameters
							parser.add_argument(
							    """--data_dir"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""The input data dir. Should contain data files for WikiText."""     ,	)
							parser.add_argument(
							    """--model_name_or_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to pretrained model or model identifier from huggingface.co/models"""     ,	)
							parser.add_argument(
							    """--data_file"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_SNAKE_CASE     ,	help=(
							        """A jbl file containing tokenized data which can be split as objective dataset, """
							        """train_dataset and test_dataset."""
							    )     ,	)

							parser.add_argument(
							    """--igf_data_file"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_SNAKE_CASE     ,	help="""A jbl file containing the context and information gain pairs to train secondary learner."""     ,	)

							parser.add_argument(
							    """--output_dir"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""The output directory where the final fine-tuned model is stored."""     ,	)

							parser.add_argument(
							    """--tokenizer_name"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Pretrained tokenizer name or path if not the same as model_name"""     ,	)
							parser.add_argument("""--seed"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=__SCREAMING_SNAKE_CASE     ,	help="""A seed for reproducible training."""  )

							parser.add_argument(
							    """--context_len"""     ,	default=32     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """The maximum total input sequence length after tokenization. Sequences longer """
							        """than this will be truncated, sequences shorter will be padded."""
							    )     ,	)

							parser.add_argument(
							    """--size_objective_set"""     ,	default=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""number of articles that are long enough to be used as our objective set"""     ,	)
							parser.add_argument(
							    """--eval_freq"""     ,	default=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""secondary model evaluation is triggered at eval_freq"""  )

							parser.add_argument("""--max_steps"""     ,	default=1_000     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""To calculate training epochs"""  )

							parser.add_argument(
							    """--secondary_learner_batch_size"""     ,	default=128     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""batch size of training data for secondary learner"""     ,	)

							parser.add_argument(
							    """--batch_size"""     ,	default=16     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""batch size of training data of language model(gpt2) """  )

							parser.add_argument(
							    """--eval_interval"""     ,	default=10     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """decay the selectivity of our secondary learner filter from"""
							        """1 standard deviation above average to 1 below average after 10 batches"""
							    )     ,	)

							parser.add_argument(
							    """--number"""     ,	default=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""The number of examples split to be used as objective_set/test_data"""  )

							parser.add_argument(
							    """--min_len"""     ,	default=1_026     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""The minimum length of the article to be used as objective set"""  )

							parser.add_argument(
							    """--secondary_learner_max_epochs"""     ,	default=15     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""number of epochs to train secondary learner"""  )

							parser.add_argument("""--trim"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""truncate the example if it exceeds context length"""  )

							parser.add_argument(
							    """--threshold"""     ,	default=1.0     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """The threshold value used by secondary learner to filter the train_data and allow only"""
							        """ informative data as input to the model"""
							    )     ,	)

							parser.add_argument("""--finetuned_model_name"""     ,	default="""gpt2_finetuned.pt"""     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""finetuned_model_name"""  )

							parser.add_argument(
							    """--recopy_model"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""     ,	)

							# function calls
							# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
							generate_n_pairs(
							    context_len=32     ,	max_steps=10     ,	size_objective_set=100     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE     ,	data_file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	igf_data_file="""igf_context_pairs.jbl"""     ,	)

							# Load train data for secondary learner
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    joblib.load("""data/IGF_values.jbl"""  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						int			  =    training_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	secondary_learner_max_epochs=15     ,	secondary_learner_batch_size=128     ,	eval_freq=100     ,	igf_model_path="""igf_model.pt"""     ,	)

							# load pretrained gpt2 model
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )
							set_seed(42  )

							# Generate train and test data to train and evaluate gpt2 model
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    generate_datasets(
							    context_len=32     ,	file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	number=100     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE  )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	context_len=32     ,	max_steps=1_000     ,	batch_size=16     ,	threshold=1.0     ,	recopy_model=__SCREAMING_SNAKE_CASE     ,	secondary_learner=__SCREAMING_SNAKE_CASE     ,	eval_interval=10     ,	finetuned_model_name="""gpt2_finetuned.pt"""     ,	)


if __name__ == "__main__":
			main()


