


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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

__lowerCAmelCase	: Any  				=logging.get_logger(__name__)

__lowerCAmelCase	: str  				={
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[Any]						=		'''imagegpt'''
				SCREAMING_SNAKE_CASE__				:			int						=		['''past_key_values''']
				SCREAMING_SNAKE_CASE__				:			str						=		{
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}


				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Any=512 + 1			,			lowerCAmelCase__			:int=32 * 32			,			lowerCAmelCase__			:Optional[Any]=512			,			lowerCAmelCase__			:Union[str, Any]=24			,			lowerCAmelCase__			:Optional[Any]=8			,			lowerCAmelCase__			:List[str]=None			,			lowerCAmelCase__			:Dict="quick_gelu"			,			lowerCAmelCase__			:Optional[Any]=0.1			,			lowerCAmelCase__			:Any=0.1			,			lowerCAmelCase__			:int=0.1			,			lowerCAmelCase__			:Optional[Any]=1E-5			,			lowerCAmelCase__			:Any=0.02			,			lowerCAmelCase__			:Any=True			,			lowerCAmelCase__			:Optional[Any]=True			,			lowerCAmelCase__			:Optional[Any]=False			,			lowerCAmelCase__			:int=False			,			lowerCAmelCase__			:Tuple=False			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Any					=		n_positions
										__SCREAMING_SNAKE_CASE						:     List[str]					=		n_embd
										__SCREAMING_SNAKE_CASE						:     int					=		n_layer
										__SCREAMING_SNAKE_CASE						:     Any					=		n_head
										__SCREAMING_SNAKE_CASE						:     Any					=		n_inner
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		activation_function
										__SCREAMING_SNAKE_CASE						:     Dict					=		resid_pdrop
										__SCREAMING_SNAKE_CASE						:     int					=		embd_pdrop
										__SCREAMING_SNAKE_CASE						:     Any					=		attn_pdrop
										__SCREAMING_SNAKE_CASE						:     str					=		layer_norm_epsilon
										__SCREAMING_SNAKE_CASE						:     str					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     List[str]					=		scale_attn_weights
										__SCREAMING_SNAKE_CASE						:     str					=		use_cache
										__SCREAMING_SNAKE_CASE						:     int					=		scale_attn_by_inverse_layer_idx
										__SCREAMING_SNAKE_CASE						:     List[str]					=		reorder_and_upcast_attn
										__SCREAMING_SNAKE_CASE						:     Dict					=		tie_word_embeddings

										super().__init__(tie_word_embeddings=lowerCAmelCase__			,			**lowerCAmelCase__	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										    ]	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"FeatureExtractionMixin"			,			lowerCAmelCase__			:int = 1			,			lowerCAmelCase__			:int = -1			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional["TensorType"] = None			,			lowerCAmelCase__			:int = 3			,			lowerCAmelCase__			:int = 32			,			lowerCAmelCase__			:int = 32			,			)		->			Mapping[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._generate_dummy_images(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		dict(preprocessor(images=lowerCAmelCase__			,			return_tensors=lowerCAmelCase__	)	)

										return inputs








"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'       )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'       )

      super().__init__(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor_class







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


