
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
_SCREAMING_SNAKE_CASE    :      Any          =			'src/transformers'


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

_SCREAMING_SNAKE_CASE    :      str          =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE    :      Tuple          =			{
    # 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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	str         =							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
												):
																A__  :	int         =							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}\""							,						__UpperCamelCase							,						)
												    is not None
												):
																A__  :	Any         =							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:
																				A__  :	Any         =							True
												if attribute_used:
																break
								if attribute_used:
												break

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

				# Special cases to be allowed
				A__  :	Optional[Any]         =							True
				if not attribute_used:
								A__  :	int         =							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:
																A__  :	Any         =							True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																A__  :	Dict         =							True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																A__  :	Optional[int]         =							True
												elif attribute.endswith('''_token_id'''       ):
																A__  :	Union[str, Any]         =							True

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

				return attribute_used or case_allowed

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							dict(inspect.signature(config_class.__init__       ).parameters       )
				A__  :	Optional[Any]         =							[x for x in list(signature.keys()       ) if x not in ['''self''', '''kwargs''']]
				A__  :	int         =							[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
				A__  :	Optional[Any]         =							{}
				if len(config_class.attribute_map       ) > 0:
								A__  :	List[Any]         =							{v: k for k, v in config_class.attribute_map.items()}

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

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

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

				return sorted(__UpperCamelCase       )

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

				"""simple docstring"""







				A__  :	Dict         =							{}
				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.)
								A__  :	Tuple         =							[
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class       )							,						lambda __UpperCamelCase       : inspect.isclass(__UpperCamelCase       )
								        and issubclass(__UpperCamelCase							,						__UpperCamelCase       )
								        and inspect.getmodule(__UpperCamelCase       ) == inspect.getmodule(_config_class       )							,						)
								]
								for config_class in config_classes_in_module:
												A__  :	List[str]         =							check_config_attributes_being_used(__UpperCamelCase       )
												if len(__UpperCamelCase       ) > 0:
																A__  :	Union[str, Any]         =							unused_attributes

				if len(__UpperCamelCase       ) > 0:
								A__  :	Optional[Any]         =							'''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(__UpperCamelCase       )


if __name__ == "__main__":
					check_config_attributes()

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids