





"""simple docstring"""


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_="attention"      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						str    =							params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      _UpperCamelCase   :						List[Any]    =							params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      _UpperCamelCase   :						Tuple    =							params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      _UpperCamelCase   :						Any    =							params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=False      )       ->		Tuple:




      """simple docstring"""

      if split_mlp_wi:
            _UpperCamelCase   :						int    =							params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            _UpperCamelCase   :						Optional[int]    =							params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            _UpperCamelCase   :						int    =							(wi_a, wi_a)
      else:
            _UpperCamelCase   :						int    =							params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      _UpperCamelCase   :						Optional[Any]    =							params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']




def        lowercase__     (       lowercase_		,*, lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							traverse_util.flatten_dict(variables["target"]      )
      _UpperCamelCase   :						str    =							{"/".join(lowercase_      ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      _UpperCamelCase   :						int    =							"encoder/layers_0/mlp/wi_0/kernel" in old
      print("Split MLP:"		,lowercase_      )

      _UpperCamelCase   :						Optional[int]    =							collections.OrderedDict()

      # Shared embeddings.
      _UpperCamelCase   :						Any    =							old["token_embedder/embedding"]

      # Encoder.
      for i in range(lowercase_      ):
            # Block i, layer 0 (Self Attention).
            _UpperCamelCase   :						str    =							tax_layer_norm_lookup(lowercase_		,lowercase_		,"encoder"		,"pre_attention_layer_norm"      )
            _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[str]    =							tax_attention_lookup(lowercase_		,lowercase_		,"encoder"		,"attention"      )
            _UpperCamelCase   :						Union[str, Any]    =							layer_norm
            _UpperCamelCase   :						Tuple    =							k.T
            _UpperCamelCase   :						Any    =							o.T
            _UpperCamelCase   :						Dict    =							q.T
            _UpperCamelCase   :						List[Any]    =							v.T

            # Block i, layer 1 (MLP).
            _UpperCamelCase   :						Any    =							tax_layer_norm_lookup(lowercase_		,lowercase_		,"encoder"		,"pre_mlp_layer_norm"      )
            _UpperCamelCase,	_UpperCamelCase   :						str    =							tax_mlp_lookup(lowercase_		,lowercase_		,"encoder"		,lowercase_      )
            _UpperCamelCase   :						List[Any]    =							layer_norm
            if split_mlp_wi:
                  _UpperCamelCase   :						Optional[Any]    =							wi[0].T
                  _UpperCamelCase   :						str    =							wi[1].T
            else:
                  _UpperCamelCase   :						Optional[int]    =							wi.T
            _UpperCamelCase   :						Tuple    =							wo.T

      _UpperCamelCase   :						Tuple    =							old[
          "encoder/relpos_bias/rel_embedding"
      ].T
      _UpperCamelCase   :						Tuple    =							old["encoder/encoder_norm/scale"]

      if not is_encoder_only:
            # Decoder.
            for i in range(lowercase_      ):
                  # Block i, layer 0 (Self Attention).
                  _UpperCamelCase   :						Dict    =							tax_layer_norm_lookup(lowercase_		,lowercase_		,"decoder"		,"pre_self_attention_layer_norm"      )
                  _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Tuple    =							tax_attention_lookup(lowercase_		,lowercase_		,"decoder"		,"self_attention"      )
                  _UpperCamelCase   :						List[Any]    =							layer_norm
                  _UpperCamelCase   :						str    =							k.T
                  _UpperCamelCase   :						Tuple    =							o.T
                  _UpperCamelCase   :						Optional[Any]    =							q.T
                  _UpperCamelCase   :						List[str]    =							v.T

                  # Block i, layer 1 (Cross Attention).
                  _UpperCamelCase   :						List[str]    =							tax_layer_norm_lookup(lowercase_		,lowercase_		,"decoder"		,"pre_cross_attention_layer_norm"      )
                  _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Tuple    =							tax_attention_lookup(lowercase_		,lowercase_		,"decoder"		,"encoder_decoder_attention"      )
                  _UpperCamelCase   :						str    =							layer_norm
                  _UpperCamelCase   :						Any    =							k.T
                  _UpperCamelCase   :						str    =							o.T
                  _UpperCamelCase   :						List[str]    =							q.T
                  _UpperCamelCase   :						Union[str, Any]    =							v.T

                  # Block i, layer 2 (MLP).
                  _UpperCamelCase   :						str    =							tax_layer_norm_lookup(lowercase_		,lowercase_		,"decoder"		,"pre_mlp_layer_norm"      )
                  _UpperCamelCase,	_UpperCamelCase   :						int    =							tax_mlp_lookup(lowercase_		,lowercase_		,"decoder"		,lowercase_      )
                  _UpperCamelCase   :						Dict    =							layer_norm
                  if split_mlp_wi:
                        _UpperCamelCase   :						Tuple    =							wi[0].T
                        _UpperCamelCase   :						List[Any]    =							wi[1].T
                  else:
                        _UpperCamelCase   :						int    =							wi.T
                  _UpperCamelCase   :						str    =							wo.T

            _UpperCamelCase   :						Optional[int]    =							old["decoder/decoder_norm/scale"]
            _UpperCamelCase   :						Tuple    =							old[
                "decoder/relpos_bias/rel_embedding"
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  _UpperCamelCase   :						str    =							old["decoder/logits_dense/kernel"].T

      return new




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            _UpperCamelCase   :						Union[str, Any]    =							state_dict["shared.weight"]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  _UpperCamelCase   :						Optional[int]    =							state_dict["shared.weight"]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("Using shared word embeddings as lm_head."      )
                  _UpperCamelCase   :						List[str]    =							state_dict["shared.weight"]

      return state_dict




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							checkpoints.load_tax_checkpoint(lowercase_      )
      _UpperCamelCase   :						List[str]    =							convert_tax_to_pytorch(lowercase_		,num_layers=config.num_layers		,is_encoder_only=lowercase_      )
      _UpperCamelCase   :						Optional[int]    =							make_state_dict(lowercase_		,lowercase_      )
      model.load_state_dict(lowercase_		,strict=lowercase_      )




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_ = False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						str    =							TaConfig.from_json_file(lowercase_      )
      print(F'''Building PyTorch model from configuration: {config}'''      )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            _UpperCamelCase   :						Optional[int]    =							TaEncoderModel(lowercase_      )
      else:
            _UpperCamelCase   :						List[Any]    =							TaForConditionalGeneration(lowercase_      )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowercase_		,lowercase_		,lowercase_		,lowercase_      )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
      model.save_pretrained(lowercase_      )

      # Verify that we can load the checkpoint.
      model.from_pretrained(lowercase_      )
      print("Done"      )


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
  # Required parameters
  parser.add_argument(
      "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
  )
  lowerCamelCase__						= parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )







"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
        return center_crop(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              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:
              raise ValueError("Size must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop 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:
              _UpperCamelCase   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

        if do_resize:
              _UpperCamelCase   :						Union[str, Any]    =							[self.resize(image=__a       , size=__a       , resample=__a						) for image in images]

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

        if do_rescale:
              _UpperCamelCase   :						Dict    =							[self.rescale(image=__a       , scale=__a						) for image in images]

        if do_normalize:
              _UpperCamelCase   :						List[Any]    =							[self.normalize(image=__a       , mean=__a       , std=__a						) for image in images]

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
