


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
A							:					List[Any]     						=     'src/transformers'


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

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

A							:					List[str]     						=     {
    # 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       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		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
																		):
																								__snake_case				=		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}"'''      ,	_lowerCAmelCase      ,	)
																		    is not None
																		):
																								__snake_case				=		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:
																														__snake_case				=		True
																		if attribute_used:
																								break
												if attribute_used:
																		break

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

						# Special cases to be allowed
						__snake_case				=		True
						if not attribute_used:
												__snake_case				=		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:
																								__snake_case				=		True
																		elif attribute in ["tie_word_embeddings"] and default_value is False:
																								__snake_case				=		True

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

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

						return attribute_used or case_allowed





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		dict(inspect.signature(config_class.__init__ ).parameters )
						__snake_case				=		[x for x in list(signature.keys() ) if x not in ["self", "kwargs"]]
						__snake_case				=		[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
						__snake_case				=		{}
						if len(config_class.attribute_map ) > 0:
												__snake_case				=		{v: k for k, v in config_class.attribute_map.items()}

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

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

						__snake_case				=		[]
						for config_param, default_value in zip(_lowerCAmelCase      ,	_lowerCAmelCase ):
												# `attributes` here is all the variant names for `config_param`
												__snake_case				=		[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(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase ):
																		unused_attributes.append(attributes[0] )

						return sorted(_lowerCAmelCase )





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






						__snake_case				=		{}
						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.)
												__snake_case				=		[
												    cls
												    for name, cls in inspect.getmembers(
												        inspect.getmodule(_config_class )      ,	lambda _lowerCAmelCase : inspect.isclass(_lowerCAmelCase )
												        and issubclass(_lowerCAmelCase      ,	_lowerCAmelCase )
												        and inspect.getmodule(_lowerCAmelCase ) == inspect.getmodule(_config_class )      ,	)
												]
												for config_class in config_classes_in_module:
																		__snake_case				=		check_config_attributes_being_used(_lowerCAmelCase )
																		if len(_lowerCAmelCase ) > 0:
																								__snake_case				=		unused_attributes

						if len(_lowerCAmelCase ) > 0:
												__snake_case				=		"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(_lowerCAmelCase )


if __name__ == "__main__":
	check_config_attributes()









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					int     						=     {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""blenderbot-small"""
					snake_case_		:				List[Any]										=	["""past_key_values"""]
					snake_case_		:				Any										=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=5_0_2_6_5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=8     ,    SCREAMING_SNAKE_CASE	:		int=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Any=1_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		str=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    SCREAMING_SNAKE_CASE	:		Any=2     ,    **SCREAMING_SNAKE_CASE	:		Dict     ,    )			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		d_model
											__snake_case				=		encoder_ffn_dim
											__snake_case				=		encoder_layers
											__snake_case				=		encoder_attention_heads
											__snake_case				=		decoder_ffn_dim
											__snake_case				=		decoder_layers
											__snake_case				=		decoder_attention_heads
											__snake_case				=		dropout
											__snake_case				=		attention_dropout
											__snake_case				=		activation_dropout
											__snake_case				=		activation_function
											__snake_case				=		init_std
											__snake_case				=		encoder_layerdrop
											__snake_case				=		decoder_layerdrop
											__snake_case				=		use_cache
											__snake_case				=		encoder_layers
											__snake_case				=		scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    is_encoder_decoder=SCREAMING_SNAKE_CASE     ,    decoder_start_token_id=SCREAMING_SNAKE_CASE     ,    forced_eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )






class     UpperCamelCase(  _a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
																	__snake_case				=		OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]						)

																	if self.use_past:
																							__snake_case				=		{0: "batch"}
																							__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}

																	if self.use_past:
																							self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE     ,    direction="inputs"						)
											elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	__snake_case				=		OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]						)
																	if self.use_past:
																							__snake_case		,					__snake_case				=		self.num_layers
																							for i in range(SCREAMING_SNAKE_CASE						):
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
											else:
																	__snake_case				=		OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																	        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																	    ]						)

											return common_inputs


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
																	__snake_case				=		super().outputs
											else:
																	__snake_case				=		super(SCREAMING_SNAKE_CASE     ,    self						).outputs
																	if self.use_past:
																							__snake_case		,					__snake_case				=		self.num_layers
																							for i in range(SCREAMING_SNAKE_CASE						):
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
																													__snake_case				=		{0: "batch", 2: "past_sequence + sequence"}
											return common_outputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											# Generate decoder inputs
											__snake_case				=		seq_length if not self.use_past else 1
											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
											__snake_case				=		dict(**SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											if self.use_past:
																	if not is_torch_available():
																							raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
																	else:
																							import torch
																	__snake_case		,					__snake_case				=		common_inputs["input_ids"].shape
																	__snake_case				=		common_inputs["decoder_input_ids"].shape[1]
																	__snake_case		,					__snake_case				=		self.num_attention_heads
																	__snake_case				=		(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	__snake_case				=		decoder_seq_length + 3
																	__snake_case				=		(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	__snake_case				=		torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)]     ,    dim=1						)

																	__snake_case				=		[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	__snake_case		,					__snake_case				=		self.num_layers
																	__snake_case				=		min(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																	__snake_case				=		max(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) - min_num_layers
																	__snake_case				=		"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																	for _ in range(SCREAMING_SNAKE_CASE						):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(SCREAMING_SNAKE_CASE						),
																							        torch.zeros(SCREAMING_SNAKE_CASE						),
																							        torch.zeros(SCREAMING_SNAKE_CASE						),
																							        torch.zeros(SCREAMING_SNAKE_CASE						),
																							    )						)
																	# TODO: test this.
																	__snake_case				=		encoder_shape if remaining_side_name == "encoder" else decoder_shape
																	for _ in range(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																							common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE						), torch.zeros(SCREAMING_SNAKE_CASE						))						)
											return common_inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											if self.use_past:
																	if not is_torch_available():
																							raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
																	else:
																							import torch
																	__snake_case		,					__snake_case				=		common_inputs["input_ids"].shape
																	# Not using the same length for past_key_values
																	__snake_case				=		seqlen + 2
																	__snake_case		,					__snake_case				=		self.num_layers
																	__snake_case		,					__snake_case				=		self.num_attention_heads
																	__snake_case				=		(
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	__snake_case				=		common_inputs["attention_mask"].dtype
																	__snake_case				=		torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    dtype=SCREAMING_SNAKE_CASE						)]     ,    dim=1						)
																	__snake_case				=		[
																	    (torch.zeros(SCREAMING_SNAKE_CASE						), torch.zeros(SCREAMING_SNAKE_CASE						)) for _ in range(SCREAMING_SNAKE_CASE						)
																	]
											return common_inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_SNAKE_CASE     ,    fixed_dimension=OnnxConfig.default_fixed_batch     ,    num_token_to_add=0						)

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__snake_case				=		tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE						)
											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_SNAKE_CASE     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=SCREAMING_SNAKE_CASE						)

											# Generate dummy inputs according to compute batch and sequence
											__snake_case				=		[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
											__snake_case				=		dict(tokenizer(SCREAMING_SNAKE_CASE     ,    return_tensors=SCREAMING_SNAKE_CASE						)						)
											return common_inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
																	__snake_case				=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											elif self.task == "causal-lm":
																	__snake_case				=		self._generate_dummy_inputs_for_causal_lm(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											return common_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

											if self.task in ["default", "seq2seq-lm"]:
																	__snake_case				=		super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		super(SCREAMING_SNAKE_CASE     ,    self						)._flatten_past_key_values_(
																	    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





