


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Tuple      =['image_processor', 'tokenizer']
      UpperCamelCase__							:  List[str]      ='AutoImageProcessor'
      UpperCamelCase__							:  List[str]      ='AutoTokenizer'






      def __init__(					self						:     List[Any]			,   lowercase_						:     Tuple=None			,   lowercase_						:     List[Any]=None			,   **lowercase_						:     Optional[int] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'			,   lowercase_			,   )
                    _lowerCamelCase       :		Tuple		=kwargs.pop('feature_extractor' )

             _lowerCamelCase       :		Any		=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__(lowercase_			,   lowercase_ )
             _lowerCamelCase       :		Any		=self.image_processor
             _lowerCamelCase       :		int		=False






      def __call__(					self						:     str			,   *lowercase_						:     Dict			,   **lowercase_						:     List[str] )					->						Tuple:
             """simple docstring"""




             if self._in_target_context_manager:
                    return self.current_processor(*lowercase_			,   **lowercase_ )

             _lowerCamelCase       :		List[Any]		=kwargs.pop('images'			,   lowercase_ )
             _lowerCamelCase       :		Dict		=kwargs.pop('text'			,   lowercase_ )
             if len(lowercase_ ) > 0:
                    _lowerCamelCase       :		Optional[int]		=args[0]
                    _lowerCamelCase       :		int		=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:
                    _lowerCamelCase       :		List[str]		=self.image_processor(lowercase_			,   *lowercase_			,   **lowercase_ )
             if text is not None:
                    _lowerCamelCase       :		Optional[int]		=self.tokenizer(lowercase_			,   **lowercase_ )

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






      def        lowerCamelCase       (					self						:     str			,   *lowercase_						:     Union[str, Any]			,   **lowercase_						:     Dict )					->						List[Any]:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   *lowercase_						:     Optional[int]			,   **lowercase_						:     List[str] )					->						Union[str, Any]:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_			,   **lowercase_ )






      @contextmanager
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Any:
             """simple docstring"""




             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.' )
             _lowerCamelCase       :		List[Any]		=True
             _lowerCamelCase       :		Union[str, Any]		=self.tokenizer
             yield
             _lowerCamelCase       :		List[Any]		=self.image_processor
             _lowerCamelCase       :		Any		=False






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     List[Any]			,   lowercase_						:     Optional[int]=False			,   lowercase_						:     List[Any]=None )					->						Tuple:
             """simple docstring"""




             if added_vocab is None:
                    _lowerCamelCase       :		int		=self.tokenizer.get_added_vocab()

             _lowerCamelCase       :		List[Any]		={}

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

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

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






      @property
      def        lowerCamelCase       (					self						:     int )					->						Optional[int]:
             """simple docstring"""




             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,   lowercase_			,   )
             return self.image_processor_class






      @property
      def        lowerCamelCase       (					self						:     int )					->						List[Any]:
             """simple docstring"""




             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,   lowercase_			,   )
             return self.image_processor






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		Dict		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Union[str, Any]		=self.in_channels if i == 0 else self.out_channels

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Dict		=FlaxTransformeraDModel(
                        in_channels=self.out_channels			,   n_heads=self.num_attention_heads			,   d_head=self.out_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   only_cross_attention=self.only_cross_attention			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

             _lowerCamelCase       :		Optional[int]		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_downsample:
                    _lowerCamelCase       :		Union[str, Any]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[str]=True )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=()

             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    _lowerCamelCase       :		Union[str, Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Optional[int]		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Tuple		=self.in_channels if i == 0 else self.out_channels

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=resnets

             if self.add_downsample:
                    _lowerCamelCase       :		List[str]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[Any]=True )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=()

             for resnet in self.resnets:
                    _lowerCamelCase       :		Tuple		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Tuple		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		List[str]		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		Tuple		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		List[str]		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxTransformeraDModel(
                        in_channels=self.out_channels			,   n_heads=self.num_attention_heads			,   d_head=self.out_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   only_cross_attention=self.only_cross_attention			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

             _lowerCamelCase       :		int		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_upsample:
                    _lowerCamelCase       :		str		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Optional[Any]			,   lowercase_						:     Any			,   lowercase_						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]=True )					->						Optional[int]:
             """simple docstring"""




             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    # pop res hidden states
                    _lowerCamelCase       :		Optional[int]		=res_hidden_states_tuple[-1]
                    _lowerCamelCase       :		Union[str, Any]		=res_hidden_states_tuple[:-1]
                    _lowerCamelCase       :		Any		=jnp.concatenate((hidden_states, res_hidden_states)			,   axis=-1 )

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Optional[Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Tuple		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		int		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		str		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		str		=resnets

             if self.add_upsample:
                    _lowerCamelCase       :		List[str]		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     List[str]			,   lowercase_						:     str			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Any=True )					->						int:
             """simple docstring"""




             for resnet in self.resnets:
                    # pop res hidden states
                    _lowerCamelCase       :		List[str]		=res_hidden_states_tuple[-1]
                    _lowerCamelCase       :		str		=res_hidden_states_tuple[:-1]
                    _lowerCamelCase       :		List[str]		=jnp.concatenate((hidden_states, res_hidden_states)			,   axis=-1 )

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Union[str, Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[
                 FlaxResnetBlockaD(
                     in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
             ]

             _lowerCamelCase       :		Any		=[]

             for _ in range(self.num_layers ):
                    _lowerCamelCase       :		Optional[int]		=FlaxTransformeraDModel(
                        in_channels=self.in_channels			,   n_heads=self.num_attention_heads			,   d_head=self.in_channels // self.num_attention_heads			,   depth=1			,   use_linear_projection=self.use_linear_projection			,   use_memory_efficient_attention=self.use_memory_efficient_attention			,   dtype=self.dtype			,   )
                    attentions.append(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxResnetBlockaD(
                        in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		List[Any]		=resnets
             _lowerCamelCase       :		List[str]		=attentions






      def __call__(					self						:     Optional[int]			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=True )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.resnets[0](lowercase_			,   lowercase_ )
             for attn, resnet in zip(self.attentions			,   self.resnets[1:] ):
                    _lowerCamelCase       :		Tuple		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[str]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             return hidden_states


