




'''simple docstring'''


from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
 import torch







class     SCREAMING_SNAKE_CASE						(	lowerCAmelCase_							):
      __magic_name__     :					Optional[Any]             =				['image_processor', 'tokenizer']
      __magic_name__     :					Optional[int]             =				'OneFormerImageProcessor'
      __magic_name__     :					Optional[Any]             =				('CLIPTokenizer', 'CLIPTokenizerFast')






      def __init__(   self      :     List[str]	,	lowercase__      :     List[Any]=None	,	lowercase__      :     Union[str, Any]=None	,	lowercase__      :     Optional[int] = 77	,	lowercase__      :     List[str] = 77	,	**lowercase__      :     List[str]					):


           '''simple docstring'''

           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`."""					)

           a_    :       Union[str, Any]    	=     max_seq_length
           a_    :       str    	=     task_seq_length

           super().__init__(lowerCamelCase_	,	lowerCamelCase_					)






      def        lowercase_	(   self      :     Tuple	,	lowercase__      :     List[Any]=None	,	lowercase__      :     Dict=77					):


           '''simple docstring'''

           if text_list is None:
                raise ValueError("""tokens cannot be None."""					)

           a_    :       Optional[int]    	=     self.tokenizer(lowerCamelCase_	,	padding="""max_length"""	,	max_length=lowerCamelCase_	,	truncation=lowerCamelCase_					)

           a_	,    a_    :       Any    	=     tokens["""attention_mask"""], tokens["""input_ids"""]

           a_    :       Tuple    	=     []
           for attn_mask, input_id in zip(lowerCamelCase_	,	lowerCamelCase_					):
                a_    :       int    	=     torch.tensor(lowerCamelCase_					) * torch.tensor(lowerCamelCase_					)
                token_inputs.append(token.unsqueeze(0					)					)

           a_    :       int    	=     torch.cat(lowerCamelCase_	,	dim=0					)
           return token_inputs






      def __call__(   self      :     str	,	lowercase__      :     Optional[int]=None	,	lowercase__      :     str=None	,	lowercase__      :     List[str]=None	,	**lowercase__      :     str					):


           '''simple docstring'''

           if task_inputs is None:
                raise ValueError("""You have to specify the task_input. Found None."""					)
           elif images is None:
                raise ValueError("""You have to specify the image. Found None."""					)

           if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs					):
                raise ValueError("""task_inputs must be semantic, instance, or panoptic."""					)

           a_    :       Any    	=     self.image_processor(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	**lowerCamelCase_					)

           if isinstance(lowerCamelCase_	,	lowerCamelCase_					):
                a_    :       Any    	=     [task_inputs]

           if isinstance(lowerCamelCase_	,	lowerCamelCase_					) and all(isinstance(lowerCamelCase_	,	lowerCamelCase_					) for task_input in task_inputs					):
                a_    :       Any    	=     []
                for task in task_inputs:
                     a_    :       Union[str, Any]    	=     F"the task is {task}"
                     task_token_inputs.append(lowerCamelCase_					)
                a_    :       Optional[int]    	=     self._preprocess_text(lowerCamelCase_	,	max_length=self.task_seq_length					)
           else:
                raise TypeError("""Task Inputs should be a string or a list of strings."""					)

           if hasattr(lowerCamelCase_	,	"""text_inputs"""					):
                a_    :       Dict    	=     encoded_inputs.text_inputs

                a_    :       Any    	=     []
                for texts in texts_list:
                     a_    :       Dict    	=     self._preprocess_text(lowerCamelCase_	,	max_length=self.max_seq_length					)
                     text_inputs.append(text_input_list.unsqueeze(0					)					)

                a_    :       Dict    	=     torch.cat(lowerCamelCase_	,	dim=0					)

           return encoded_inputs






      def        lowercase_	(   self      :     Any	,	lowercase__      :     int=None	,	lowercase__      :     int=None	,	lowercase__      :     Optional[Any]=None	,	**lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           if task_inputs is None:
                raise ValueError("""You have to specify the task_input. Found None."""					)
           elif images is None:
                raise ValueError("""You have to specify the image. Found None."""					)

           if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs					):
                raise ValueError("""task_inputs must be semantic, instance, or panoptic."""					)

           a_    :       Dict    	=     self.image_processor.encode_inputs(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	**lowerCamelCase_					)

           if isinstance(lowerCamelCase_	,	lowerCamelCase_					):
                a_    :       Any    	=     [task_inputs]

           if isinstance(lowerCamelCase_	,	lowerCamelCase_					) and all(isinstance(lowerCamelCase_	,	lowerCamelCase_					) for task_input in task_inputs					):
                a_    :       Union[str, Any]    	=     []
                for task in task_inputs:
                     a_    :       Tuple    	=     F"the task is {task}"
                     task_token_inputs.append(lowerCamelCase_					)
                a_    :       Tuple    	=     self._preprocess_text(lowerCamelCase_	,	max_length=self.task_seq_length					)
           else:
                raise TypeError("""Task Inputs should be a string or a list of strings."""					)

           if hasattr(lowerCamelCase_	,	"""text_inputs"""					):
                a_    :       List[str]    	=     encoded_inputs.text_inputs

                a_    :       Optional[int]    	=     []
                for texts in texts_list:
                     a_    :       Any    	=     self._preprocess_text(lowerCamelCase_	,	max_length=self.max_seq_length					)
                     text_inputs.append(text_input_list.unsqueeze(0					)					)

                a_    :       Tuple    	=     torch.cat(lowerCamelCase_	,	dim=0					)

           return encoded_inputs






      def        lowercase_	(   self      :     List[Any]	,	*lowercase__      :     List[str]	,	**lowercase__      :     int					):


           '''simple docstring'''

           return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase_	,	**lowerCamelCase_					)






      def        lowercase_	(   self      :     List[Any]	,	*lowercase__      :     Union[str, Any]	,	**lowercase__      :     int					):


           '''simple docstring'''

           return self.image_processor.post_process_instance_segmentation(*lowerCamelCase_	,	**lowerCamelCase_					)






      def        lowercase_	(   self      :     Optional[Any]	,	*lowercase__      :     int	,	**lowercase__      :     List[str]					):


           '''simple docstring'''

           return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase_	,	**lowerCamelCase_					)






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_    :       Optional[Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

          if split_mlp_wi:
               a_    :       Tuple    	=     tax_mlp_wi_a
               a_    :       Dict    	=     tax_mlp_wi_a
          else:
               a_    :       Optional[Any]    	=     tax_mlp_wi

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

     # Only for layer 0:
     a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Union[str, Any]    	=     tax_encoder_rel_embedding

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_    :       int    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

          # Encoder-Decoder-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          a_    :       str    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_    :       List[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

          if split_mlp_wi:
               a_    :       int    	=     tax_mlp_wi_a
               a_    :       Optional[int]    	=     tax_mlp_wi_a
          else:
               a_    :       Tuple    	=     tax_mlp_wi

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

     # Only for layer 0:
     a_    :       Dict    	=     tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Any    	=     tax_decoder_rel_embedding

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          a_    :       int    	=     tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

     flax_model.save_pretrained(UpperCamelCase__							)
     print("""T5X Model was sucessfully converted!"""							)


if __name__ == "__main__":
 lowerCAmelCase_			:     List[Any]							    =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 lowerCAmelCase_			:     str							    =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
