

"""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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="attention"   ):
      UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
      UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
      UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
      UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
      return k, o, q, v
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):




      if split_mlp_wi:
            UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
            UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
            UpperCAmelCase_    				=							(wi_a, wi_a)
      else:
            UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

      UpperCAmelCase_    				=							params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
      return wi, wo
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def      a__			(   lowerCAmelCase__			,    *, lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							traverse_util.flatten_dict(variables["target"]   )
      UpperCAmelCase_    				=							{"/".join(_lowerCAmelCase   ): v for k, v in old.items()}

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

      UpperCAmelCase_    				=							collections.OrderedDict()

      # Shared embeddings.
      UpperCAmelCase_    				=							old["token_embedder/embedding"]

      # Encoder.
      for i in range(_lowerCAmelCase   ):
            # Block i, layer 0 (Self Attention).
            UpperCAmelCase_    				=							tax_layer_norm_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "encoder"			,    "pre_attention_layer_norm"   )
            UpperCAmelCase_    				=							tax_attention_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "encoder"			,    "attention"   )
            UpperCAmelCase_    				=							layer_norm
            UpperCAmelCase_    				=							k.T
            UpperCAmelCase_    				=							o.T
            UpperCAmelCase_    				=							q.T
            UpperCAmelCase_    				=							v.T

            # Block i, layer 1 (MLP).
            UpperCAmelCase_    				=							tax_layer_norm_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "encoder"			,    "pre_mlp_layer_norm"   )
            UpperCAmelCase_    				=							tax_mlp_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "encoder"			,    _lowerCAmelCase   )
            UpperCAmelCase_    				=							layer_norm
            if split_mlp_wi:
                  UpperCAmelCase_    				=							wi[0].T
                  UpperCAmelCase_    				=							wi[1].T
            else:
                  UpperCAmelCase_    				=							wi.T
            UpperCAmelCase_    				=							wo.T

      UpperCAmelCase_    				=							old[
          "encoder/relpos_bias/rel_embedding"
      ].T
      UpperCAmelCase_    				=							old["encoder/encoder_norm/scale"]

      if not is_encoder_only:
            # Decoder.
            for i in range(_lowerCAmelCase   ):
                  # Block i, layer 0 (Self Attention).
                  UpperCAmelCase_    				=							tax_layer_norm_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    "pre_self_attention_layer_norm"   )
                  UpperCAmelCase_    				=							tax_attention_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    "self_attention"   )
                  UpperCAmelCase_    				=							layer_norm
                  UpperCAmelCase_    				=							k.T
                  UpperCAmelCase_    				=							o.T
                  UpperCAmelCase_    				=							q.T
                  UpperCAmelCase_    				=							v.T

                  # Block i, layer 1 (Cross Attention).
                  UpperCAmelCase_    				=							tax_layer_norm_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    "pre_cross_attention_layer_norm"   )
                  UpperCAmelCase_    				=							tax_attention_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    "encoder_decoder_attention"   )
                  UpperCAmelCase_    				=							layer_norm
                  UpperCAmelCase_    				=							k.T
                  UpperCAmelCase_    				=							o.T
                  UpperCAmelCase_    				=							q.T
                  UpperCAmelCase_    				=							v.T

                  # Block i, layer 2 (MLP).
                  UpperCAmelCase_    				=							tax_layer_norm_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    "pre_mlp_layer_norm"   )
                  UpperCAmelCase_    				=							tax_mlp_lookup(_lowerCAmelCase			,    _lowerCAmelCase			,    "decoder"			,    _lowerCAmelCase   )
                  UpperCAmelCase_    				=							layer_norm
                  if split_mlp_wi:
                        UpperCAmelCase_    				=							wi[0].T
                        UpperCAmelCase_    				=							wi[1].T
                  else:
                        UpperCAmelCase_    				=							wi.T
                  UpperCAmelCase_    				=							wo.T

            UpperCAmelCase_    				=							old["decoder/decoder_norm/scale"]
            UpperCAmelCase_    				=							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_    				=							old["decoder/logits_dense/kernel"].T

      return new
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							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_    				=							state_dict["shared.weight"]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  UpperCAmelCase_    				=							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_    				=							state_dict["shared.weight"]

      return state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							checkpoints.load_tax_checkpoint(_lowerCAmelCase   )
      UpperCAmelCase_    				=							convert_tax_to_pytorch(_lowerCAmelCase			,    num_layers=config.num_layers			,    is_encoder_only=_lowerCAmelCase   )
      UpperCAmelCase_    				=							make_state_dict(_lowerCAmelCase			,    _lowerCAmelCase   )
      model.load_state_dict(_lowerCAmelCase			,    strict=_lowerCAmelCase   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							TaConfig.from_json_file(_lowerCAmelCase   )
      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_    				=							TaEncoderModel(_lowerCAmelCase   )
      else:
            UpperCAmelCase_    				=							TaForConditionalGeneration(_lowerCAmelCase   )

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

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
      model.save_pretrained(_lowerCAmelCase   )

      # Verify that we can load the checkpoint.
      model.from_pretrained(_lowerCAmelCase   )
      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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowerCamelCase      =		logging.get_logger(__name__)
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						str	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 384}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             # Default value set here for backwards compatibility where the value in config is None
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else 224 / 256
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						float	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BICUBIC	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" not in size:
                   raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
             UpperCAmelCase_    				=							size["shortest_edge"]

             if shortest_edge < 384:
                   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                   UpperCAmelCase_    				=							int(shortest_edge / crop_pct					)
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size=_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
                   # then crop to (shortest_edge, shortest_edge)
                   return center_crop(image=_UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   # warping (no cropping) when evaluated at 384 or larger
                   return resize(
                       _UpperCAmelCase	,       size=(shortest_edge, shortest_edge)	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[int, float]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[Any]	,       )				->		Any:


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						List[str]	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       _UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							crop_pct if crop_pct is not None else self.crop_pct
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)

             UpperCAmelCase_    				=							make_list_of_images(_UpperCAmelCase					)

             if not valid_images(_UpperCAmelCase					):
                   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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                   raise ValueError("crop_pct must be specified if size < 384."					)

             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."					)

             # All transformations expect numpy arrays.
             UpperCAmelCase_    				=							[to_numpy_array(_UpperCAmelCase					) for image in images]

             if do_resize:
                   UpperCAmelCase_    				=							[self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       crop_pct=_UpperCAmelCase	,       resample=_UpperCAmelCase					) for image in images]

             if do_rescale:
                   UpperCAmelCase_    				=							[self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					) for image in images]

             if do_normalize:
                   UpperCAmelCase_    				=							[self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							[to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					) for image in images]

             UpperCAmelCase_    				=							{"pixel_values": images}
             return BatchFeature(data=_UpperCAmelCase	,       tensor_type=_UpperCAmelCase					)




