'''simple docstring'''


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	if "model" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''model.'''    ,					''''''       )
	if "norm1" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''norm1'''    ,					'''attention.output.LayerNorm'''       )
	if "norm2" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''norm2'''    ,					'''output.LayerNorm'''       )
	if "norm" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''norm'''    ,					'''LayerNorm'''       )
	if "transformer" in orig_key:
		__UpperCAmelCase							=			orig_key.split('''.'''       )[0].split('''_'''       )[-1]
		__UpperCAmelCase							=			orig_key.replace(f"""transformer_{layer_num}"""    ,					f"""encoder.layer.{layer_num}"""       )
	if "mha.attn" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''mha.attn'''    ,					'''attention.self'''       )
	if "mha" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''mha'''    ,					'''attention'''       )
	if "W_q" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''W_q'''    ,					'''self.query'''       )
	if "W_k" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''W_k'''    ,					'''self.key'''       )
	if "W_v" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''W_v'''    ,					'''self.value'''       )
	if "ff1" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''ff1'''    ,					'''intermediate.dense'''       )
	if "ff2" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''ff2'''    ,					'''output.dense'''       )
	if "ff" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''ff'''    ,					'''output.dense'''       )
	if "mlm_class" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''mlm.mlm_class'''    ,					'''cls.predictions.decoder'''       )
	if "mlm" in orig_key:
		__UpperCAmelCase							=			orig_key.replace('''mlm'''    ,					'''cls.predictions.transform'''       )
	if "cls" not in orig_key:
		__UpperCAmelCase							=			'''yoso.''' + orig_key

	return orig_key





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	for key in orig_state_dict.copy().keys():
		__UpperCAmelCase							=			orig_state_dict.pop(UpperCamelCase__       )

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			__UpperCAmelCase							=			val

	__UpperCAmelCase							=			orig_state_dict['''cls.predictions.decoder.bias''']
	__UpperCAmelCase							=			torch.arange(UpperCamelCase__       ).expand((1, -1)       ) + 2

	return orig_state_dict





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			torch.load(UpperCamelCase__    ,					map_location='''cpu'''       )['''model_state_dict''']
	__UpperCAmelCase							=			YosoConfig.from_json_file(UpperCamelCase__       )
	__UpperCAmelCase							=			YosoForMaskedLM(UpperCamelCase__       )

	__UpperCAmelCase							=			convert_checkpoint_helper(config.max_position_embeddings    ,					UpperCamelCase__       )

	print(model.load_state_dict(UpperCamelCase__       )       )
	model.eval()
	model.save_pretrained(UpperCamelCase__       )

	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
					)
					parser.add_argument(
					    "--config_file",
					    default=None,
					    type=str,
					    required=True,
					    help="The json file for YOSO model config.",
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase	:       Any						       =	parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)






class 						A   :





							def __init__(      self				:							List[Any]     ,		__a				:							Any     ,		)							->	Dict:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			1_3
								__UpperCAmelCase							=			7
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			False
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			9_9
								__UpperCAmelCase							=			3_2
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			3_7
								__UpperCAmelCase							=			'''gelu'''
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			5_1_2
								__UpperCAmelCase							=			1_6
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			0.0_2
								__UpperCAmelCase							=			3
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			None





							def       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

								__UpperCAmelCase							=			None
								if self.use_input_mask:
									__UpperCAmelCase							=			random_attention_mask([self.batch_size, self.seq_length]	)

								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								if self.use_labels:
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.num_choices	)

								__UpperCAmelCase							=			DistilBertConfig(
								    vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							List[str]     ,		__a				:							int     ,		__a				:							Union[str, Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]     ,		__a				:							int	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModel(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}

								__UpperCAmelCase							=			model(__a	)

								__UpperCAmelCase							=			[input_ids, input_mask]

								__UpperCAmelCase							=			model(__a	)

								self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			TFDistilBertForMaskedLM(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Any     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							int     ,		__a				:							Optional[Any]     ,		__a				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertForQuestionAnswering(config=__a	)
								__UpperCAmelCase							=			{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								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       snake_case__						(      self				:							Any     ,		__a				:							Optional[Any]     ,		__a				:							List[str]     ,		__a				:							Dict     ,		__a				:							Dict     ,		__a				:							int     ,		__a				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForSequenceClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int     ,		__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							List[str]     ,		__a				:							Dict	)							->	str:
								__UpperCAmelCase							=			self.num_choices
								__UpperCAmelCase							=			TFDistilBertForMultipleChoice(__a	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			{
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)	)





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							int     ,		__a				:							Optional[int]     ,		__a				:							Optional[int]	)							->	int:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForTokenClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)






							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			self.prepare_config_and_inputs()
								((__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase))							=			config_and_inputs
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_tf
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (
							    (
							        TFDistilBertModel,
							        TFDistilBertForMaskedLM,
							        TFDistilBertForQuestionAnswering,
							        TFDistilBertForSequenceClassification,
							        TFDistilBertForTokenClassification,
							        TFDistilBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else None
							)
							a_        =      (
							    {
							        '''feature-extraction''': TFDistilBertModel,
							        '''fill-mask''': TFDistilBertForMaskedLM,
							        '''question-answering''': TFDistilBertForQuestionAnswering,
							        '''text-classification''': TFDistilBertForSequenceClassification,
							        '''token-classification''': TFDistilBertForTokenClassification,
							        '''zero-shot''': TFDistilBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModelTester(self	)
								__UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		dim=3_7	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								self.config_tester.run_common_tests()





							def       snake_case__						(      self				:							Any	)							->	str:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_model(*__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_masked_lm(*__a	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_question_answering(*__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_sequence_classification(*__a	)





							def       snake_case__						(      self				:							Any	)							->	int:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_multiple_choice(*__a	)





							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_token_classification(*__a	)






							@slow
							def       snake_case__						(      self				:							Dict	)							->	Tuple:
								for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__UpperCAmelCase							=			TFDistilBertModel.from_pretrained(__a	)
									self.assertIsNotNone(__a	)






@require_tf
class 						A   (   unittest.TestCase   ):





							@slow
							def       snake_case__						(      self				:							int	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''	)
								__UpperCAmelCase							=			tf.constant([[0, 1, 2, 3, 4, 5]]	)
								__UpperCAmelCase							=			model(__a	)[0]

								__UpperCAmelCase							=			[1, 6, 7_6_8]
								self.assertEqual(output.shape     ,		__a	)

								__UpperCAmelCase							=			tf.constant(
								    [
								        [
								            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
								            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
								            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
								        ]
								    ]	)
								tf.debugging.assert_near(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)
