





'''simple docstring'''

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def    _a				(							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     AutoConfig.from_pretrained(lowerCAmelCase_	)
      _snake_case		:	Optional[int]					     =     FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

      _snake_case		:	str					     =     '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            _snake_case		:	Union[str, Any]					     =     '''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            _snake_case		:	Tuple					     =     '''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            _snake_case		:	int					     =     '''TransientGlobalSelfAttention'''
      else:
            raise ValueError(
                '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                ''' attribute with a value from [\'local\', \'transient-global].'''	)

      # Encoder
      for layer_index in range(config.num_layers	):
            _snake_case		:	Optional[Any]					     =     f'''layers_{str(lowerCAmelCase_	)}'''

            # Self-Attention
            _snake_case		:	Optional[int]					     =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            _snake_case		:	List[str]					     =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            _snake_case		:	List[Any]					     =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            _snake_case		:	Optional[int]					     =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  _snake_case		:	Union[str, Any]					     =     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            _snake_case		:	Dict					     =     tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  _snake_case		:	List[str]					     =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  _snake_case		:	Dict					     =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  _snake_case		:	Any					     =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            _snake_case		:	Any					     =     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            _snake_case		:	str					     =     tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            _snake_case		:	int					     =     flax_model.params['''encoder''']['''block'''][str(lowerCAmelCase_	)]['''layer''']
            _snake_case		:	str					     =     tax_attention_key
            _snake_case		:	Dict					     =     tax_attention_out
            _snake_case		:	Optional[Any]					     =     tax_attention_query
            _snake_case		:	int					     =     tax_attention_value

            _snake_case		:	List[str]					     =     tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  _snake_case		:	Tuple					     =     tax_global_layer_norm

            if split_mlp_wi:
                  _snake_case		:	List[Any]					     =     tax_mlp_wi_a
                  _snake_case		:	Any					     =     tax_mlp_wi_a
            else:
                  _snake_case		:	int					     =     tax_mlp_wi

            _snake_case		:	Any					     =     tax_mlp_wo
            _snake_case		:	Optional[Any]					     =     tax_mlp_layer_norm

            _snake_case		:	Optional[Any]					     =     flax_model_encoder_layer_block

      # Only for layer 0:
      _snake_case		:	Optional[Any]					     =     tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      _snake_case		:	List[Any]					     =     tax_encoder_rel_embedding

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            _snake_case		:	List[str]					     =     tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            _snake_case		:	str					     =     tax_encoder_global_rel_embedding

      # Assigning
      _snake_case		:	Tuple					     =     tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      _snake_case		:	str					     =     tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers	):
            _snake_case		:	Union[str, Any]					     =     f'''layers_{str(lowerCAmelCase_	)}'''

            # Self-Attention
            _snake_case		:	Union[str, Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            _snake_case		:	Tuple					     =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            _snake_case		:	List[str]					     =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            _snake_case		:	Union[str, Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            _snake_case		:	Any					     =     tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            _snake_case		:	int					     =     tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  _snake_case		:	Optional[Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  _snake_case		:	List[Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  _snake_case		:	Optional[int]					     =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            _snake_case		:	Optional[Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            _snake_case		:	Union[str, Any]					     =     tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            _snake_case		:	Optional[int]					     =     flax_model.params['''decoder''']['''block'''][str(lowerCAmelCase_	)]['''layer''']
            _snake_case		:	Tuple					     =     tax_attention_key
            _snake_case		:	Union[str, Any]					     =     tax_attention_out
            _snake_case		:	Union[str, Any]					     =     tax_attention_query
            _snake_case		:	Tuple					     =     tax_attention_value

            _snake_case		:	List[str]					     =     tax_pre_attention_layer_norm

            _snake_case		:	List[Any]					     =     tax_enc_dec_attention_key
            _snake_case		:	Optional[int]					     =     tax_enc_dec_attention_out
            _snake_case		:	List[str]					     =     tax_enc_dec_attention_query
            _snake_case		:	List[str]					     =     tax_enc_dec_attention_value

            _snake_case		:	List[str]					     =     tax_cross_layer_norm

            if split_mlp_wi:
                  _snake_case		:	List[Any]					     =     tax_mlp_wi_a
                  _snake_case		:	List[Any]					     =     tax_mlp_wi_a
            else:
                  _snake_case		:	Any					     =     tax_mlp_wi

            _snake_case		:	Dict					     =     tax_mlp_wo

            _snake_case		:	int					     =     txa_mlp_layer_norm

            _snake_case		:	Any					     =     flax_model_decoder_layer_block

      # Decoder Normalization
      _snake_case		:	Optional[Any]					     =     tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      _snake_case		:	List[Any]					     =     txa_decoder_norm

      # Only for layer 0:
      _snake_case		:	Dict					     =     tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      _snake_case		:	int					     =     tax_decoder_rel_embedding

      # Token Embeddings
      _snake_case		:	Any					     =     tax_model['''target''']['''token_embedder''']['''embedding''']
      _snake_case		:	Any					     =     txa_token_embeddings

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            _snake_case		:	Union[str, Any]					     =     tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

      flax_model.save_pretrained(lowerCAmelCase_	)
      print('''T5X Model was sucessfully converted!'''	)


if __name__ == "__main__":
   UpperCAmelCase					:		List[str]		=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
   )
   parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
   parser.add_argument(
       '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
   )
   UpperCAmelCase					:		List[Any]		=							parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = 32     ,     lowercase__=PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        return rescale(image=lowercase__     ,     scale=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError('''size_divisor is required for resizing'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              raise ValueError('''Invalid image(s)'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


