import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=13      ,	SCREAMING_SNAKE_CASE__    :					List[str]=7      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Any=True      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=99      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=32      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=5      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=4      ,	SCREAMING_SNAKE_CASE__    :					Dict=37      ,	SCREAMING_SNAKE_CASE__    :					Dict="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=50      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=True      ,	SCREAMING_SNAKE_CASE__    :					Tuple=None      ,	)    ->				List[Any]:
         A						:     int         =parent
         A						:     Union[str, Any]         =batch_size
         A						:     Tuple         =seq_length
         A						:     List[Any]         =is_training
         A						:     Optional[Any]         =use_input_mask
         A						:     str         =vocab_size
         A						:     Optional[int]         =hidden_size
         A						:     Optional[int]         =num_hidden_layers
         A						:     List[str]         =num_attention_heads
         A						:     Dict         =intermediate_size
         A						:     str         =hidden_act
         A						:     Tuple         =hidden_dropout_prob
         A						:     Union[str, Any]         =attention_probs_dropout_prob
         A						:     Optional[Any]         =max_position_embeddings
         A						:     Optional[Any]         =initializer_range
         A						:     List[str]         =use_labels
         A						:     Union[str, Any]         =scope







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         A						:     Union[str, Any]         =ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size     )

         A						:     Dict         =None
         if self.use_input_mask:
                A						:     Union[str, Any]         =random_attention_mask([self.batch_size, self.seq_length]     )

         if self.use_labels:
                A						:     Optional[Any]         =ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size     )

         A						:     str         =self.get_config()

         return config, input_ids, input_mask, token_labels







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         return BertGenerationConfig(
             vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	is_decoder=SCREAMING_SNAKE_CASE__      ,	initializer_range=self.initializer_range      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Any:
         (
             (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  
         )						:     Optional[Any]         =self.prepare_config_and_inputs()

         A						:     str         =True
         A						:     Tuple         =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
         A						:     Union[str, Any]         =ids_tensor([self.batch_size, self.seq_length]      ,	vocab_size=2     )

         return (
             config,
             input_ids,
             input_mask,
             token_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	)    ->				List[str]:
         A						:     Tuple         =BertGenerationEncoder(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     Any         =model(SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =model(SCREAMING_SNAKE_CASE__     )
         self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					int      ,	)    ->				List[str]:
         A						:     str         =True
         A						:     str         =BertGenerationEncoder(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     Optional[int]         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,	encoder_attention_mask=SCREAMING_SNAKE_CASE__      ,	)
         A						:     Optional[Any]         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,	)
         self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	)    ->				Optional[int]:
         A						:     int         =True
         A						:     Optional[Any]         =True
         A						:     Union[str, Any]         =BertGenerationDecoder(config=SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     ).eval()

         # first forward pass
         A						:     int         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,	encoder_attention_mask=SCREAMING_SNAKE_CASE__      ,	use_cache=SCREAMING_SNAKE_CASE__      ,	)
         A						:     Optional[int]         =outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         A						:     Any         =ids_tensor((self.batch_size, 3)      ,	config.vocab_size     )
         A						:     Optional[int]         =ids_tensor((self.batch_size, 3)      ,	vocab_size=2     )

         # append to next input_ids and
         A						:     List[str]         =torch.cat([input_ids, next_tokens]      ,	dim=-1     )
         A						:     int         =torch.cat([input_mask, next_mask]      ,	dim=-1     )

         A						:     Union[str, Any]         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,	encoder_attention_mask=SCREAMING_SNAKE_CASE__      ,	output_hidden_states=SCREAMING_SNAKE_CASE__      ,	)['hidden_states'][0]
         A						:     List[str]         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,	encoder_attention_mask=SCREAMING_SNAKE_CASE__      ,	past_key_values=SCREAMING_SNAKE_CASE__      ,	output_hidden_states=SCREAMING_SNAKE_CASE__      ,	)['hidden_states'][0]

         # select random slice
         A						:     Optional[Any]         =ids_tensor((1,)      ,	output_from_past.shape[-1]     ).item()
         A						:     Dict         =output_from_no_past[:, -3:, random_slice_idx].detach()
         A						:     List[Any]         =output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	*SCREAMING_SNAKE_CASE__    :					List[str]      ,	)    ->				int:
         A						:     Optional[int]         =BertGenerationDecoder(SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     Optional[Any]         =model(SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__     )
         self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.vocab_size)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Tuple:
         A  ,  A  ,  A  ,  A						:     Optional[Any]         =self.prepare_config_and_inputs()
         A						:     List[Any]         ={'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						lowerCAmelCase_   ,						lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[str]            =				(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
  lowercase		:     Optional[Any]            =				(BertGenerationDecoder,) if is_torch_available() else ()
  lowercase		:     Dict            =				(
      {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
      if is_torch_available()
      else {}
  )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[Any]:
         A						:     Union[str, Any]         =BertGenerationEncoderTester(self     )
         A						:     int         =ConfigTester(self      ,	config_class=SCREAMING_SNAKE_CASE__      ,	hidden_size=37     )







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				str:
         A						:     Union[str, Any]         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         A  ,  A  ,  A  ,  A						:     Dict         =self.model_tester.prepare_config_and_inputs()
         A						:     Any         ='bert'
         self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				str:
         A						:     str         =self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Dict:
         A						:     Any         =self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[int]:
         # This regression test was failing with PyTorch < 1.3
         (
             (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  (
             A
         )  ,  
         )						:     Any         =self.model_tester.prepare_config_and_inputs_for_decoder()

         A						:     Union[str, Any]         =None

         self.model_tester.create_and_check_model_as_decoder(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Dict:
         A						:     Union[str, Any]         =self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				int:
         A						:     Optional[Any]         =BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )
         self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )







@require_torch
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[Any]:
         A						:     Optional[int]         =BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )
         A						:     List[str]         =torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]     )
         with torch.no_grad():
                A						:     Optional[int]         =model(SCREAMING_SNAKE_CASE__     )[0]
         A						:     Tuple         =torch.Size([1, 8, 10_24]     )
         self.assertEqual(output.shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     List[Any]         =torch.tensor(
             [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]     )
         self.assertTrue(torch.allclose(output[:, :3, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )







@require_torch
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         A						:     Tuple         =BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )
         A						:     Union[str, Any]         =torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]     )
         with torch.no_grad():
                A						:     Dict         =model(SCREAMING_SNAKE_CASE__     )[0]
         A						:     Optional[int]         =torch.Size([1, 8, 5_03_58]     )
         self.assertEqual(output.shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     int         =torch.tensor(
             [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]     )
         self.assertTrue(torch.allclose(output[:, :3, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






