
'''simple docstring'''
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 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Dict=13  ,	UpperCamelCase__				:			Dict=7  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			Any=99  ,	UpperCamelCase__				:			Optional[int]=32  ,	UpperCamelCase__				:			str=5  ,	UpperCamelCase__				:			List[Any]=4  ,	UpperCamelCase__				:			List[str]=37  ,	UpperCamelCase__				:			Dict="gelu"  ,	UpperCamelCase__				:			str=0.1  ,	UpperCamelCase__				:			Any=0.1  ,	UpperCamelCase__				:			Optional[int]=50  ,	UpperCamelCase__				:			Optional[Any]=0.02  ,	UpperCamelCase__				:			List[Any]=True  ,	UpperCamelCase__				:			Any=None  ,	)						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						parent
				snake_case  :       Tuple     			=						batch_size
				snake_case  :       Dict     			=						seq_length
				snake_case  :       Union[str, Any]     			=						is_training
				snake_case  :       Tuple     			=						use_input_mask
				snake_case  :       Union[str, Any]     			=						vocab_size
				snake_case  :       Union[str, Any]     			=						hidden_size
				snake_case  :       Dict     			=						num_hidden_layers
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       List[str]     			=						intermediate_size
				snake_case  :       List[str]     			=						hidden_act
				snake_case  :       Dict     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[Any]     			=						max_position_embeddings
				snake_case  :       str     			=						initializer_range
				snake_case  :       List[str]     			=						use_labels
				snake_case  :       List[Any]     			=						scope



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size      )

				snake_case  :       Optional[int]     			=						None
				if self.use_input_mask:
						snake_case  :       str     			=						random_attention_mask([self.batch_size, self.seq_length]      )

				if self.use_labels:
						snake_case  :       Union[str, Any]     			=						ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size      )

				snake_case  :       str     			=						self.get_config()

				return config, input_ids, input_mask, token_labels



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Union[str, Any]:






				"""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       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				(
				    (
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,
				)  :       Any     			=						self.prepare_config_and_inputs()

				snake_case  :       Optional[Any]     			=						True
				snake_case  :       Union[str, Any]     			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
				snake_case  :       Optional[int]     			=						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       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Any  ,	**UpperCamelCase__				:			Union[str, Any]  ,	)						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						BertGenerationEncoder(config=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       Optional[Any]     			=						model(UpperCamelCase__  ,	attention_mask=UpperCamelCase__      )
				snake_case  :       int     			=						model(UpperCamelCase__      )
				self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size)      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			str  ,	**UpperCamelCase__				:			Union[str, Any]  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       Dict     			=						True
				snake_case  :       Tuple     			=						BertGenerationEncoder(config=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       Optional[Any]     			=						model(
				    UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	encoder_attention_mask=UpperCamelCase__  ,	)
				snake_case  :       Any     			=						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       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Optional[int]  ,	**UpperCamelCase__				:			int  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						True
				snake_case  :       List[Any]     			=						True
				snake_case  :       Union[str, Any]     			=						BertGenerationDecoder(config=UpperCamelCase__      ).to(UpperCamelCase__      ).eval()

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

				# create hypothetical multiple next token and extent to next_input_ids
				snake_case  :       List[str]     			=						ids_tensor((self.batch_size, 3)  ,	config.vocab_size      )
				snake_case  :       Dict     			=						ids_tensor((self.batch_size, 3)  ,	vocab_size=2      )

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

				snake_case  :       Optional[Any]     			=						model(
				    UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	encoder_attention_mask=UpperCamelCase__  ,	output_hidden_states=UpperCamelCase__  ,	)['''hidden_states'''][0]
				snake_case  :       List[str]     			=						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
				snake_case  :       int     			=						ids_tensor((1,)  ,	output_from_past.shape[-1]      ).item()
				snake_case  :       List[str]     			=						output_from_no_past[:, -3:, random_slice_idx].detach()
				snake_case  :       Optional[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(UpperCamelCase__  ,	UpperCamelCase__  ,	atol=1e-3      )      )



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			str  ,	*UpperCamelCase__				:			Optional[Any]  ,	)						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						BertGenerationDecoder(UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	labels=UpperCamelCase__      )
				self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)      )



		def       lowerCAmelCase				(      self				:			str      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Optional[Any]     			=						self.prepare_config_and_inputs()
				snake_case  :       str     			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict







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







		"""simple docstring"""

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



		def       lowerCAmelCase				(      self				:			str      )						->			int:






				"""simple docstring"""







				snake_case  :       Dict     			=						BertGenerationEncoderTester(self      )
				snake_case  :       Optional[int]     			=						ConfigTester(self  ,	config_class=UpperCamelCase__  ,	hidden_size=37      )



		def       lowerCAmelCase				(      self				:			Any      )						->			List[Any]:






				"""simple docstring"""







				self.config_tester.run_common_tests()



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Optional[int]     			=						self.model_tester.prepare_config_and_inputs()
				snake_case  :       Union[str, Any]     			=						'''bert'''
				self.model_tester.create_and_check_model(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple      )						->			List[Any]:






				"""simple docstring"""







				(
				    (
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,
				)  :       Any     			=						self.model_tester.prepare_config_and_inputs_for_decoder()

				snake_case  :       int     			=						None

				self.model_tester.create_and_check_model_as_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	)



		def       lowerCAmelCase				(      self				:			Any      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase__      )



		@slow
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Tuple     			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''      )
				self.assertIsNotNone(UpperCamelCase__      )







@require_torch
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			int      )						->			Any:






				"""simple docstring"""







				snake_case  :       Tuple     			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''      )
				snake_case  :       Dict     			=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
				with torch.no_grad():
						snake_case  :       List[Any]     			=						model(UpperCamelCase__      )[0]
				snake_case  :       Tuple     			=						torch.Size([1, 8, 1024]      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						torch.tensor(
				    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]      )
				self.assertTrue(torch.allclose(output[:, :3, :3]  ,	UpperCamelCase__  ,	atol=1e-4      )      )







@require_torch
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       int     			=						BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''      )
				snake_case  :       Any     			=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
				with torch.no_grad():
						snake_case  :       int     			=						model(UpperCamelCase__      )[0]
				snake_case  :       Dict     			=						torch.Size([1, 8, 5_0358]      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						torch.tensor(
				    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]      )
				self.assertTrue(torch.allclose(output[:, :3, :3]  ,	UpperCamelCase__  ,	atol=1e-4      )      )




'''simple docstring'''
import os
from collections.abc import Iterator




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							Iterator[str]:


		'''simple docstring'''


		for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Optional[Any]     			=						[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(SCREAMING_SNAKE_CASE__			)[1] in (".py", ".ipynb"):
								yield os.path.join(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).lstrip('''./'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		return F'{i * "  "}*' if i else "\n##"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						old_path.split(os.sep			)
		for i, new_part in enumerate(new_path.split(os.sep			)			):
				if (i + 1 > len(SCREAMING_SNAKE_CASE__			) or old_parts[i] != new_part) and new_part:
						print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} {new_part.replace("_" ,       " "			).title()}'			)
		return new_path




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							None:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						''''''
		for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE__			)			):
				snake_case			,snake_case  :       Optional[Any]     			=						os.path.split(SCREAMING_SNAKE_CASE__			)
				if filepath != old_path:
						snake_case  :       Dict     			=						print_path(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						(filepath.count(os.sep			) + 1) if filepath else 0
				snake_case  :       int     			=						F'{filepath}/{filename}'.replace(''' ''' ,       '''%20'''			)
				snake_case  :       int     			=						os.path.splitext(filename.replace('''_''' ,       ''' '''			).title()			)[0]
				print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} [{filename}]({url})'			)


if __name__ == "__main__":
						print_directory_md(".")


