






import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase			:		str   				=    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase			:		List[Any]   				=    direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCAmelCase			:		List[str]   				=    transformers.models.auto.configuration_auto.CONFIG_MAPPING

_UpperCAmelCase			:		Any   				=    {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       Dict     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Dict       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						False
				for attribute in attributes:
								for modeling_source in source_strings:
												# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
												if (
												    f'config.{attribute}' in modeling_source
												    or f'getattr(config, "{attribute}"' in modeling_source
												    or f'getattr(self.config, "{attribute}"' in modeling_source
												):
																lowerCAmelCase__      			=						True
												# Deal with multi-line cases
												elif (
												    re.search(
												        rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'     ,					lowercase__     ,					)
												    is not None
												):
																lowerCAmelCase__      			=						True
												# `SequenceSummary` is called with `SequenceSummary(config)`
												elif attribute in [
												    "summary_type",
												    "summary_use_proj",
												    "summary_activation",
												    "summary_last_dropout",
												    "summary_proj_to_labels",
												    "summary_first_dropout",
												]:
																if "SequenceSummary" in modeling_source:
																				lowerCAmelCase__      			=						True
												if attribute_used:
																break
								if attribute_used:
												break

    # common and important attributes, even if they do not always appear in the modeling files
				lowerCAmelCase__      			=						[
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				lowerCAmelCase__      			=						['''encoder_no_repeat_ngram_size''']

				# Special cases to be allowed
				lowerCAmelCase__      			=						True
				if not attribute_used:
								lowerCAmelCase__      			=						False
								for attribute in attributes:
												# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
												if attribute in ["is_encoder_decoder"] and default_value is True:
																lowerCAmelCase__      			=						True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																lowerCAmelCase__      			=						True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																lowerCAmelCase__      			=						True
												elif attribute.endswith('''_token_id'''       ):
																lowerCAmelCase__      			=						True

												# configuration class specific cases
												if not case_allowed:
																lowerCAmelCase__      			=						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__     ,					[]       )
																lowerCAmelCase__      			=						allowed_cases is True or attribute in allowed_cases

				return attribute_used or case_allowed





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						dict(inspect.signature(config_class.__init__       ).parameters       )
				lowerCAmelCase__      			=						[x for x in list(signature.keys()       ) if x not in ['''self''', '''kwargs''']]
				lowerCAmelCase__      			=						[signature[param].default for param in parameter_names]

				# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
				# as one variant is used, the test should pass
				lowerCAmelCase__      			=						{}
				if len(config_class.attribute_map       ) > 0:
								lowerCAmelCase__      			=						{v: k for k, v in config_class.attribute_map.items()}

				# Get the path to modeling source files
				lowerCAmelCase__      			=						inspect.getsourcefile(lowercase__       )
				lowerCAmelCase__      			=						os.path.dirname(lowercase__       )
				# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
				lowerCAmelCase__      			=						[os.path.join(lowercase__     ,					lowercase__       ) for fn in os.listdir(lowercase__       ) if fn.startswith('''modeling_'''       )]

				# Get the source code strings
				lowerCAmelCase__      			=						[]
				for path in modeling_paths:
								if os.path.isfile(lowercase__       ):
												with open(lowercase__       ) as fp:
																modeling_sources.append(fp.read()       )

				lowerCAmelCase__      			=						[]
				for config_param, default_value in zip(lowercase__     ,					lowercase__       ):
								# `attributes` here is all the variant names for `config_param`
								lowerCAmelCase__      			=						[config_param]
								# some configuration classes have non-empty `attribute_map`, and both names could be used in the
								# corresponding modeling files. As long as one of them appears, it is fine.
								if config_param in reversed_attribute_map:
												attributes.append(reversed_attribute_map[config_param]       )

								if not check_attribute_being_used(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       ):
												unused_attributes.append(attributes[0]       )

				return sorted(lowercase__       )





def 	lowerCAmelCase_		()		->    Any:


				'''simple docstring'''




				lowerCAmelCase__      			=						{}
				for _config_class in list(CONFIG_MAPPING.values()       ):
								# Skip deprecated models
								if "models.deprecated" in _config_class.__module__:
												continue
								# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
								lowerCAmelCase__      			=						[
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class       )     ,					lambda lowercase__       : inspect.isclass(lowercase__       )
								        and issubclass(lowercase__     ,					lowercase__       )
								        and inspect.getmodule(lowercase__       ) == inspect.getmodule(_config_class       )     ,					)
								]
								for config_class in config_classes_in_module:
												lowerCAmelCase__      			=						check_config_attributes_being_used(lowercase__       )
												if len(lowercase__       ) > 0:
																lowerCAmelCase__      			=						unused_attributes

				if len(lowercase__       ) > 0:
								lowerCAmelCase__      			=						'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
								for name, attributes in configs_with_unused_attributes.items():
												error += f'{name}: {attributes}\n'

								raise ValueError(lowercase__       )


if __name__ == "__main__":
			check_config_attributes()













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_       :




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=13 ,							SCREAMING_SNAKE_CASE_     :   Dict=7 ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=99 ,							SCREAMING_SNAKE_CASE_     :   str=32 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=5 ,							SCREAMING_SNAKE_CASE_     :   str=4 ,							SCREAMING_SNAKE_CASE_     :   Tuple=37 ,							SCREAMING_SNAKE_CASE_     :   List[Any]="gelu" ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=50 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.02 ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_input_mask
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_labels
											lowerCAmelCase__      			=						scope




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

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

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

											lowerCAmelCase__      			=						self.get_config()

											return config, input_ids, input_mask, token_labels




							def 						__snake_case					(   self     :   List[str]	):
											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 						__snake_case					(   self     :   str	):
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.prepare_config_and_inputs()

											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)	)




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()

											# first forward pass
											lowerCAmelCase__      			=						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_ ,							)
											lowerCAmelCase__      			=						outputs.past_key_values

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

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

											lowerCAmelCase__      			=						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]
											lowerCAmelCase__      			=						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
											lowerCAmelCase__      			=						ids_tensor((1,) ,							output_from_past.shape[-1]	).item()
											lowerCAmelCase__      			=						output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							*SCREAMING_SNAKE_CASE_     :   Tuple ,							):
											lowerCAmelCase__      			=						BertGenerationDecoder(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict



@require_torch
class  lowerCAmelCase_       (				snake_case__     ,    snake_case__     ,    snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							UpperCamelCase_		:str		      =    (BertGenerationDecoder,) if is_torch_available() else ()
							UpperCamelCase_		:List[str]		      =    (
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						BertGenerationEncoderTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							hidden_size=37	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											self.config_tester.run_common_tests()




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase__      			=						'''bert'''
											self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any	):
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.model_tester.prepare_config_and_inputs_for_decoder()

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_	)




							@slow
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 1_024]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 50_358]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)





