
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__="attention"		):
      UpperCamelCase__     : Tuple											=					np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]		)
      UpperCamelCase__     : Optional[Any]											=					k_tmp.reshape(k_tmp.shape[0]			,		k_tmp.shape[1] * k_tmp.shape[2]		)
      UpperCamelCase__     : Optional[int]											=					np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]		)
      UpperCamelCase__     : Dict											=					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,		o_tmp.shape[2]		)
      UpperCamelCase__     : Dict											=					np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]		)
      UpperCamelCase__     : Optional[int]											=					q_tmp.reshape(q_tmp.shape[0]			,		q_tmp.shape[1] * q_tmp.shape[2]		)
      UpperCamelCase__     : Optional[int]											=					np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]		)
      UpperCamelCase__     : Union[str, Any]											=					v_tmp.reshape(v_tmp.shape[0]			,		v_tmp.shape[1] * v_tmp.shape[2]		)
      return k, o, q, v


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False		):
      if split_mlp_wi:
            UpperCamelCase__     : Optional[int]											=					params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            UpperCamelCase__     : List[str]											=					params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            UpperCamelCase__     : Dict											=					(wi_a, wi_a)
      else:
            UpperCamelCase__     : Any											=					params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      UpperCamelCase__     : List[str]											=					params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		*, UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False		):
      UpperCamelCase__     : Tuple											=					traverse_util.flatten_dict(variables['''target''']		)
      UpperCamelCase__     : str											=					{'''/'''.join(UpperCamelCase__		): 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/encoder/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''			,		UpperCamelCase__		)

      UpperCamelCase__     : int											=					collections.OrderedDict()

      # Shared embeddings.
      UpperCamelCase__     : int											=					old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCamelCase__		):
            # Block i, layer 0 (Self Attention).
            UpperCamelCase__     : Dict											=					tax_layer_norm_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''encoder'''			,		'''pre_attention_layer_norm'''		)
            UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Any											=					tax_attention_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''encoder'''			,		'''attention'''		)
            UpperCamelCase__     : List[str]											=					layer_norm
            UpperCamelCase__     : Any											=					k.T
            UpperCamelCase__     : Optional[int]											=					o.T
            UpperCamelCase__     : int											=					q.T
            UpperCamelCase__     : str											=					v.T

            # Block i, layer 1 (MLP).
            UpperCamelCase__     : str											=					tax_layer_norm_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''encoder'''			,		'''pre_mlp_layer_norm'''		)
            UpperCamelCase__					,UpperCamelCase__     : List[Any]											=					tax_mlp_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''encoder'''			,		UpperCamelCase__		)
            UpperCamelCase__     : List[Any]											=					layer_norm
            if split_mlp_wi:
                  UpperCamelCase__     : Dict											=					wi[0].T
                  UpperCamelCase__     : List[str]											=					wi[1].T
            else:
                  UpperCamelCase__     : int											=					wi.T
            UpperCamelCase__     : Dict											=					wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  UpperCamelCase__     : str											=					tax_relpos_bias_lookup(
                      UpperCamelCase__			,		UpperCamelCase__			,		'''encoder'''		).T

      UpperCamelCase__     : List[str]											=					old['''encoder/encoder_norm/scale''']

      if not scalable_attention:
            UpperCamelCase__     : Optional[Any]											=					tax_relpos_bias_lookup(
                UpperCamelCase__			,		0			,		'''encoder'''		).T
            UpperCamelCase__     : Optional[int]											=					tax_relpos_bias_lookup(
                UpperCamelCase__			,		0			,		'''decoder'''		).T

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

                  # Block i, layer 1 (Cross Attention).
                  UpperCamelCase__     : List[str]											=					tax_layer_norm_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''decoder'''			,		'''pre_cross_attention_layer_norm'''		)
                  UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : int											=					tax_attention_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''decoder'''			,		'''encoder_decoder_attention'''		)
                  UpperCamelCase__     : Optional[int]											=					layer_norm
                  UpperCamelCase__     : List[str]											=					k.T
                  UpperCamelCase__     : List[str]											=					o.T
                  UpperCamelCase__     : int											=					q.T
                  UpperCamelCase__     : str											=					v.T

                  # Block i, layer 2 (MLP).
                  UpperCamelCase__     : Tuple											=					tax_layer_norm_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''decoder'''			,		'''pre_mlp_layer_norm'''		)
                  UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					tax_mlp_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''decoder'''			,		UpperCamelCase__		)
                  UpperCamelCase__     : Optional[int]											=					layer_norm
                  if split_mlp_wi:
                        UpperCamelCase__     : List[Any]											=					wi[0].T
                        UpperCamelCase__     : List[str]											=					wi[1].T
                  else:
                        UpperCamelCase__     : Dict											=					wi.T
                  UpperCamelCase__     : int											=					wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        UpperCamelCase__     : Any											=					tax_relpos_bias_lookup(UpperCamelCase__			,		UpperCamelCase__			,		'''decoder'''		).T

            UpperCamelCase__     : Tuple											=					old['''decoder/decoder_norm/scale''']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  UpperCamelCase__     : List[Any]											=					old['''decoder/logits_dense/kernel'''].T

      return new


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Union[str, Any]											=					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__     : Optional[Any]											=					state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  UpperCamelCase__     : Optional[Any]											=					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[Any]											=					state_dict['''shared.weight''']

      return state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					checkpoints.load_tax_checkpoint(UpperCamelCase__		)
      UpperCamelCase__     : int											=					convert_tax_to_pytorch(
          UpperCamelCase__			,		num_layers=config.num_layers			,		is_encoder_only=UpperCamelCase__			,		scalable_attention=UpperCamelCase__		)
      UpperCamelCase__     : Tuple											=					make_state_dict(UpperCamelCase__			,		UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__			,		strict=UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False			,		UpperCamelCase__ = False			,		):
      UpperCamelCase__     : List[str]											=					MTaConfig.from_json_file(UpperCamelCase__		)
      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__     : Union[str, Any]											=					UMTaEncoderModel(UpperCamelCase__		)
      else:
            UpperCamelCase__     : Optional[int]											=					UMTaForConditionalGeneration(UpperCamelCase__		)

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

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

      # Verify that we can load the checkpoint.
      model.from_pretrained(UpperCamelCase__		)
      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
    )
    parser.add_argument(
        "--scalable_attention",
        action="store_true",
        help="Whether the model uses scaled attention (umt5 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,
        args.scalable_attention,
    )




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


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







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






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




           """simple docstring"""







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






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					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'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

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

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

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

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


