






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 __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=13       ,    UpperCAmelCase=7       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase=99       ,    UpperCAmelCase=32       ,    UpperCAmelCase=5       ,    UpperCAmelCase=4       ,    UpperCAmelCase=37       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=50       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=True       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			seq_length
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_input_mask
							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			hidden_size
							_UpperCAmelCase									=			num_hidden_layers
							_UpperCAmelCase									=			num_attention_heads
							_UpperCAmelCase									=			intermediate_size
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			hidden_dropout_prob
							_UpperCAmelCase									=			attention_probs_dropout_prob
							_UpperCAmelCase									=			max_position_embeddings
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			scope

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_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]    )

							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size    )

							_UpperCAmelCase									=			self.get_config()

							return config, input_ids, input_mask, token_labels

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							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=UpperCAmelCase       ,    initializer_range=self.initializer_range       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							(
							    (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, 
							)									=			self.prepare_config_and_inputs()

							_UpperCAmelCase									=			True
							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
							_UpperCAmelCase									=			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        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationEncoder(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    attention_mask=UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							_UpperCAmelCase									=			BertGenerationEncoder(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(
							    UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    encoder_attention_mask=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			model(
							    UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    )
							self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							_UpperCAmelCase									=			True
							_UpperCAmelCase									=			BertGenerationDecoder(config=UpperCAmelCase    ).to(UpperCAmelCase    ).eval()

							# first forward pass
							_UpperCAmelCase									=			model(
							    UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    encoder_attention_mask=UpperCAmelCase       ,    use_cache=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			outputs.past_key_values

							# create hypothetical multiple next token and extent to next_input_ids
							_UpperCAmelCase									=			ids_tensor((self.batch_size, 3)       ,    config.vocab_size    )
							_UpperCAmelCase									=			ids_tensor((self.batch_size, 3)       ,    vocab_size=2    )

							# append to next input_ids and
							_UpperCAmelCase									=			torch.cat([input_ids, next_tokens]       ,    dim=-1    )
							_UpperCAmelCase									=			torch.cat([input_mask, next_mask]       ,    dim=-1    )

							_UpperCAmelCase									=			model(
							    UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    encoder_attention_mask=UpperCAmelCase       ,    output_hidden_states=UpperCAmelCase       ,    )['hidden_states'][0]
							_UpperCAmelCase									=			model(
							    UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    encoder_attention_mask=UpperCAmelCase       ,    past_key_values=UpperCAmelCase       ,    output_hidden_states=UpperCAmelCase       ,    )['hidden_states'][0]

							# select random slice
							_UpperCAmelCase									=			ids_tensor((1,)       ,    output_from_past.shape[-1]    ).item()
							_UpperCAmelCase									=			output_from_no_past[:, -3:, random_slice_idx].detach()
							_UpperCAmelCase									=			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(UpperCAmelCase       ,    UpperCAmelCase       ,    atol=1e-3    )    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    *UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationDecoder(UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase									=			{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
	UpperCamelCase__								=      (BertGenerationDecoder,) if is_torch_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
	    if is_torch_available()
	    else {}
	)

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationEncoderTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    hidden_size=37    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.config_tester.run_common_tests()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							_UpperCAmelCase									=			'bert'
							self.model_tester.create_and_check_model(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							(
							    (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, 
							)									=			self.model_tester.prepare_config_and_inputs_for_decoder()

							_UpperCAmelCase									=			None

							self.model_tester.create_and_check_model_as_decoder(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'    )
							self.assertIsNotNone(UpperCAmelCase    )





@require_torch
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'    )
							_UpperCAmelCase									=			torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]    )
							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase    )[0]
							_UpperCAmelCase									=			torch.Size([1, 8, 1024]    )
							self.assertEqual(output.shape       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor(
							    [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]    )
							self.assertTrue(torch.allclose(output[:, :3, :3]       ,    UpperCAmelCase       ,    atol=1e-4    )    )





@require_torch
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'    )
							_UpperCAmelCase									=			torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]    )
							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase    )[0]
							_UpperCAmelCase									=			torch.Size([1, 8, 5_0358]    )
							self.assertEqual(output.shape       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor(
							    [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]    )
							self.assertTrue(torch.allclose(output[:, :3, :3]       ,    UpperCAmelCase       ,    atol=1e-4    )    )


import argparse
from collections import defaultdict

import yaml


__a  			=					'docs/source/en/_toctree.yml'




def 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					defaultdict(snake_case__					)
				for doc in model_doc:
								counts[doc["local"]] += 1
				lowercase_       =					[key for key, value in counts.items() if value > 1]

				lowercase_       =					[]
				for duplicate_key in duplicates:
								lowercase_       =					list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
								if len(snake_case__					) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.'''					)
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

				# Sort
				return sorted(snake_case__						,   key=lambda snake_case__					: s["title"].lower()					)




def 				a						(							snake_case__:					List[Any]=False					):


				'''simple docstring'''
				with open(snake_case__						,   encoding='''utf-8'''					) as f:
								lowercase_       =					yaml.safe_load(f.read()					)

				# Get to the API doc
				lowercase_       =					0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				lowercase_       =					content[api_idx]['''sections''']

				# Then to the model doc
				lowercase_       =					0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				lowercase_       =					api_doc[model_idx]['''sections''']

				lowercase_       =					[(idx, section) for idx, section in enumerate(snake_case__					) if '''sections''' in section]
				lowercase_       =					False
				for idx, modality_doc in modalities_docs:
								lowercase_       =					modality_doc['''sections''']
								lowercase_       =					clean_model_doc_toc(snake_case__					)

								if old_modality_doc != new_modality_doc:
												lowercase_       =					True
												if overwrite:
																lowercase_       =					new_modality_doc

				if diff:
								if overwrite:
												lowercase_       =					model_doc
												lowercase_       =					api_doc
												with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
																f.write(yaml.dump(snake_case__						,   allow_unicode=snake_case__					)					)
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a  			=					parser.parse_args()

						check_model_doc(args.fix_and_overwrite)




