





'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					=    {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ResNetForImageClassification''',
	    '''ResNetModel''',
	    '''ResNetPreTrainedModel''',
	    '''ResNetBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFResNetForImageClassification''',
	    '''TFResNetModel''',
	    '''TFResNetPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxResNetForImageClassification''',
	    '''FlaxResNetModel''',
	    '''FlaxResNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_resnet import (
		    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ResNetBackbone,
		    ResNetForImageClassification,
		    ResNetModel,
		    ResNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_resnet import (
		    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFResNetForImageClassification,
		    TFResNetModel,
		    TFResNetPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)












'''simple docstring'''



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					=    logging.get_logger(__name__)

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

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

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






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[Any]											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(
				    _lowerCamelCase     ,_lowerCamelCase     ,tokenizer_file=_lowerCamelCase     ,unk_token=_lowerCamelCase     ,bos_token=_lowerCamelCase     ,eos_token=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase     ,**_lowerCamelCase     ,)

				__lowercase										=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
				if pre_tok_state.get('''add_prefix_space'''     ,_lowerCamelCase ) != add_prefix_space:
							__lowercase										=							getattr(_lowerCamelCase     ,pre_tok_state.pop('''type''' ) )
							__lowercase										=							add_prefix_space
							__lowercase										=							pre_tok_class(**_lowerCamelCase )

				__lowercase										=							add_prefix_space


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





				__lowercase										=							self._tokenizer.model.save(_lowerCamelCase     ,name=_lowerCamelCase )
				return tuple(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[int]:







				'''simple docstring'''





				__lowercase										=							[]
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase ) + [self.eos_token_id] )

				if len(_lowerCamelCase ) > self.model_max_length:
							__lowercase										=							input_ids[-self.model_max_length :]
				return input_ids





