

'''simple docstring'''

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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
	return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]




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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
	if split_mlp_wi:
		UpperCAmelCase	:     Tuple						 =			params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		UpperCAmelCase	:     List[str]						 =			params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		UpperCAmelCase	:     Tuple						 =			(wi_a, wi_a)
	else:
		UpperCAmelCase	:     List[str]						 =			params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	UpperCAmelCase	:     str						 =			params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
	return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]




def 				UpperCamelCase(					UpperCAmelCase_  ,	*, UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = False     ):
	UpperCAmelCase	:     Dict						 =			traverse_util.flatten_dict(variables['target']     )
	UpperCAmelCase	:     str						 =			{'/'.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	:     List[Any]						 =			'encoder/encoder/mlp/wi_0/kernel' in old
	print('Split MLP:'  ,	lowerCAmelCase__     )

	UpperCAmelCase	:     Dict						 =			collections.OrderedDict()

	# Shared embeddings.
	UpperCAmelCase	:     Tuple						 =			old['token_embedder/embedding']

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

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

	UpperCAmelCase	:     Any						 =			old['encoder/encoder_norm/scale']

	if not scalable_attention:
		UpperCAmelCase	:     Tuple						 =			tax_relpos_bias_lookup(
		    lowerCAmelCase__  ,	0  ,	'encoder'     ).T
		UpperCAmelCase	:     Optional[int]						 =			tax_relpos_bias_lookup(
		    lowerCAmelCase__  ,	0  ,	'decoder'     ).T

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

			# Block i, layer 1 (Cross Attention).
			UpperCAmelCase	:     Union[str, Any]						 =			tax_layer_norm_lookup(lowerCAmelCase__  ,	lowerCAmelCase__  ,	'decoder'  ,	'pre_cross_attention_layer_norm'     )
			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			tax_attention_lookup(lowerCAmelCase__  ,	lowerCAmelCase__  ,	'decoder'  ,	'encoder_decoder_attention'     )
			UpperCAmelCase	:     Any						 =			layer_norm
			UpperCAmelCase	:     str						 =			k.T
			UpperCAmelCase	:     Tuple						 =			o.T
			UpperCAmelCase	:     Tuple						 =			q.T
			UpperCAmelCase	:     Optional[int]						 =			v.T

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

			if scalable_attention:
				# convert the rel_embedding of each layer
				UpperCAmelCase	:     Union[str, Any]						 =			tax_relpos_bias_lookup(lowerCAmelCase__  ,	lowerCAmelCase__  ,	'decoder'     ).T

		UpperCAmelCase	:     List[Any]						 =			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[str]						 =			old['decoder/logits_dense/kernel'].T

	return new




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	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	:     Optional[int]						 =			state_dict['shared.weight']

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			UpperCAmelCase	:     Dict						 =			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	:     Dict						 =			state_dict['shared.weight']

	return state_dict




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
	UpperCAmelCase	:     Dict						 =			checkpoints.load_tax_checkpoint(lowerCAmelCase__     )
	UpperCAmelCase	:     str						 =			convert_tax_to_pytorch(
	    lowerCAmelCase__  ,	num_layers=config.num_layers  ,	is_encoder_only=lowerCAmelCase__  ,	scalable_attention=lowerCAmelCase__     )
	UpperCAmelCase	:     int						 =			make_state_dict(lowerCAmelCase__  ,	lowerCAmelCase__     )
	model.load_state_dict(lowerCAmelCase__  ,	strict=lowerCAmelCase__     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = False  ,	UpperCAmelCase_ = False  ,	):
	UpperCAmelCase	:     Tuple						 =			MTaConfig.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	:     Any						 =			UMTaEncoderModel(lowerCAmelCase__     )
	else:
		UpperCAmelCase	:     List[str]						 =			UMTaForConditionalGeneration(lowerCAmelCase__     )

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(lowerCAmelCase__  ,	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__":
	lowercase__         =      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,
	)
	lowercase__         =      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,
	)







'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			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_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

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

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

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




