



'''simple docstring'''



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_lowercase       :						Any						=None


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


_lowercase       :						List[Any]						="▁"

_lowercase       :						Dict						={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase       :						Optional[int]						={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase       :						int						={
    "google/reformer-crime-and-punishment": 52_4288,
}






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  VOCAB_FILES_NAMES
			A__								=  PRETRAINED_VOCAB_FILES_MAP
			A__								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__								=  ['input_ids', 'attention_mask']
			A__								=  ReformerTokenizer



			def __init__(     self		:						int    ,			__UpperCamelCase		:						str=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Optional[int]="</s>"    ,			__UpperCamelCase		:						Dict="<unk>"    ,			__UpperCamelCase		:						Optional[int]=[]    ,			**__UpperCamelCase		:						int    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							super().__init__(
							    __UpperCamelCase    ,			tokenizer_file=__UpperCamelCase    ,			eos_token=__UpperCamelCase    ,			unk_token=__UpperCamelCase    ,			additional_special_tokens=__UpperCamelCase    ,			**__UpperCamelCase    ,			)

							snake_case__      :   List[Any]      =    vocab_file
							snake_case__      :   Union[str, Any]      =    False if not self.vocab_file else True







			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[str] = None			)      ->      Tuple[str]:
							"""simple docstring"""





							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''			)

							if not os.path.isdir(__UpperCamelCase			):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
											return
							snake_case__      :   Union[str, Any]      =    os.path.join(
							    __UpperCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

							if os.path.abspath(self.vocab_file			) != os.path.abspath(__UpperCamelCase			):
											copyfile(self.vocab_file    ,			__UpperCamelCase			)

							return (out_vocab_file,)










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a__         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTJForCausalLM''',
		    '''GPTJForQuestionAnswering''',
		    '''GPTJForSequenceClassification''',
		    '''GPTJModel''',
		    '''GPTJPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TFGPTJForCausalLM''',
		    '''TFGPTJForQuestionAnswering''',
		    '''TFGPTJForSequenceClassification''',
		    '''TFGPTJModel''',
		    '''TFGPTJPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''FlaxGPTJForCausalLM''',
		    '''FlaxGPTJModel''',
		    '''FlaxGPTJPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gptj import (
				    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTJForCausalLM,
				    GPTJForQuestionAnswering,
				    GPTJForSequenceClassification,
				    GPTJModel,
				    GPTJPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    TFGPTJPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)