






"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


lowercase__					:						Tuple												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    _A):
 _lowerCAmelCase     :    List[str]	      =	["""pixel_values"""]

 def __init__(  self	: Union[str, Any]						,  lowercase_	: List[str] = True						,  lowercase_	: Any = None						,  lowercase_	: Tuple = PILImageResampling.BICUBIC						,  lowercase_	: Optional[Any] = True						,  lowercase_	: Optional[int] = 1 / 255						,  lowercase_	: Dict = True						,  lowercase_	: Tuple = None						,  lowercase_	: List[Any] = None						,  lowercase_	: Any = True						,  **lowercase_	: List[str]						,  ):
     super().__init__(**lowercase_       )
     snake_case_				:  Optional[Any]       =			size if size is not None else {"height": 384, "width": 384}
     snake_case_				:  Tuple       =			get_size_dict(lowercase_						,  default_to_square=lowercase_       )

     snake_case_				:  int       =			do_resize
     snake_case_				:  Optional[int]       =			size
     snake_case_				:  Optional[int]       =			resample
     snake_case_				:  List[str]       =			do_rescale
     snake_case_				:  Tuple       =			rescale_factor
     snake_case_				:  Optional[Any]       =			do_normalize
     snake_case_				:  Optional[int]       =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     snake_case_				:  List[str]       =			image_std if image_std is not None else OPENAI_CLIP_STD
     snake_case_				:  Tuple       =			do_convert_rgb

 def 		_snake_case      (  self	: int						,  lowercase_	: List[Any]						,  lowercase_	: Dict						,  lowercase_	: Dict = PILImageResampling.BICUBIC						,  lowercase_	: Tuple = None						,  **lowercase_	: int						,  ):
     snake_case_				:  Optional[Any]       =			get_size_dict(lowercase_						,  default_to_square=lowercase_       )
     if "height" not in size or "width" not in size:
         raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
     snake_case_				:  str       =			(size["height"], size["width"])
     return resize(lowercase_						,  size=lowercase_						,  resample=lowercase_						,  data_format=lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: str						,  lowercase_	: str						,  lowercase_	: Optional[int]						,  lowercase_	: Any = None						,  **lowercase_	: List[Any]						,  ):
     return rescale(lowercase_						,  scale=lowercase_						,  data_format=lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Optional[int]						,  lowercase_	: List[Any]						,  lowercase_	: List[Any]						,  lowercase_	: Union[str, Any] = None						,  **lowercase_	: Any						,  ):
     return normalize(lowercase_						,  mean=lowercase_						,  std=lowercase_						,  data_format=lowercase_						,  **lowercase_       )






 def 		_snake_case      (  self	: Any						,  lowercase_	: Optional[Any]						,  lowercase_	: List[str] = None						,  lowercase_	: Optional[int] = None						,  lowercase_	: List[Any] = None						,  lowercase_	: Dict = None						,  lowercase_	: Any = None						,  lowercase_	: Union[str, Any] = None						,  lowercase_	: Optional[Any] = None						,  lowercase_	: Any = None						,  lowercase_	: Optional[int] = None						,  lowercase_	: Tuple = None						,  lowercase_	: Dict = ChannelDimension.FIRST						,  **lowercase_	: Any						,  ):
     snake_case_				:  str       =			do_resize if do_resize is not None else self.do_resize
     snake_case_				:  List[Any]       =			resample if resample is not None else self.resample
     snake_case_				:  List[Any]       =			do_rescale if do_rescale is not None else self.do_rescale
     snake_case_				:  Union[str, Any]       =			rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_				:  Any       =			do_normalize if do_normalize is not None else self.do_normalize
     snake_case_				:  int       =			image_mean if image_mean is not None else self.image_mean
     snake_case_				:  Tuple       =			image_std if image_std is not None else self.image_std
     snake_case_				:  str       =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     snake_case_				:  Optional[Any]       =			size if size is not None else self.size
     snake_case_				:  Union[str, Any]       =			get_size_dict(lowercase_						,  default_to_square=lowercase_       )

     snake_case_				:  Optional[Any]       =			make_list_of_images(lowercase_       )

     if not valid_images(lowercase_       ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

     if do_resize and size is None or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

     if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         snake_case_				:  List[Any]       =			[convert_to_rgb(lowercase_       ) for image in images]

     # All transformations expect numpy arrays.
     snake_case_				:  str       =			[to_numpy_array(lowercase_       ) for image in images]

     if do_resize:
         snake_case_				:  List[Any]       =			[self.resize(image=lowercase_						,  size=lowercase_						,  resample=lowercase_       ) for image in images]

     if do_rescale:
         snake_case_				:  int       =			[self.rescale(image=lowercase_						,  scale=lowercase_       ) for image in images]

     if do_normalize:
         snake_case_				:  Optional[int]       =			[self.normalize(image=lowercase_						,  mean=lowercase_						,  std=lowercase_       ) for image in images]

     snake_case_				:  str       =			[to_channel_dimension_format(lowercase_						,  lowercase_       ) for image in images]

     snake_case_				:  List[Any]       =			BatchFeature(data={'''pixel_values''': images}						,  tensor_type=lowercase_       )

     return encoded_outputs










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''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_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			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_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  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_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			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_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			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(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			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.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





