



'''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


_UpperCamelCase											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							snake_case_	):
  """simple docstring"""




  a_				   =["""pixel_values"""]




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

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




  def 		_lowercase	(					self :      Dict     ,      _a :      Optional[Any]     ,      _a :      Any     ,      _a :      int = PILImageResampling.BICUBIC     ,      _a :      Optional[Any] = None     ,      **_a :      Optional[Any]     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			List[str]       			=					get_size_dict(_a     ,      default_to_square=_a  )
    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()}'  )
    __lowerCamelCase			:			str       			=					(size['height'], size['width'])
    return resize(_a     ,      size=_a     ,      resample=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Tuple     ,      _a :      List[str]     ,      _a :      Tuple     ,      _a :      int = None     ,      **_a :      Dict     ,      )   ->   List[str]:
    return rescale(_a     ,      scale=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      int     ,      _a :      Dict     ,      _a :      int     ,      _a :      Dict     ,      _a :      Optional[Any] = None     ,      **_a :      Optional[Any]     ,      )   ->   np.ndarray:
    return normalize(_a     ,      mean=_a     ,      std=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Dict     ,      _a :      str     ,      _a :      Optional[int] = None     ,      _a :      Union[str, Any] = None     ,      _a :      int = None     ,      _a :      List[Any] = None     ,      _a :      Union[str, Any] = None     ,      _a :      Tuple = None     ,      _a :      Dict = None     ,      _a :      Optional[int] = None     ,      _a :      Union[str, Any] = None     ,      _a :      str = None     ,      _a :      Optional[Any] = ChannelDimension.FIRST     ,      **_a :      Tuple     ,      )   ->   PIL.Image.Image:
    __lowerCamelCase			:			List[Any]       			=					do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase			:			Dict       			=					resample if resample is not None else self.resample
    __lowerCamelCase			:			Optional[Any]       			=					do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase			:			Union[str, Any]       			=					rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase			:			List[str]       			=					do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase			:			Any       			=					image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase			:			Optional[Any]       			=					image_std if image_std is not None else self.image_std
    __lowerCamelCase			:			Dict       			=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __lowerCamelCase			:			str       			=					size if size is not None else self.size
    __lowerCamelCase			:			Dict       			=					get_size_dict(_a     ,      default_to_square=_a  )

    __lowerCamelCase			:			List[str]       			=					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 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:
      __lowerCamelCase			:			List[Any]       			=					[convert_to_rgb(_a  ) for image in images]

    # All transformations expect numpy arrays.
    __lowerCamelCase			:			List[str]       			=					[to_numpy_array(_a  ) for image in images]

    if do_resize:
      __lowerCamelCase			:			Tuple       			=					[self.resize(image=_a     ,      size=_a     ,      resample=_a  ) for image in images]

    if do_rescale:
      __lowerCamelCase			:			str       			=					[self.rescale(image=_a     ,      scale=_a  ) for image in images]

    if do_normalize:
      __lowerCamelCase			:			Any       			=					[self.normalize(image=_a     ,      mean=_a     ,      std=_a  ) for image in images]

    __lowerCamelCase			:			Union[str, Any]       			=					[to_channel_dimension_format(_a     ,      _a  ) for image in images]

    __lowerCamelCase			:			Tuple       			=					BatchFeature(data={'pixel_values': images}     ,      tensor_type=_a  )

    return encoded_outputs














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 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_="attention"):
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_=False):
							if split_mlp_wi:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
														snake_case_           =				(wi_a, wi_a)
							else:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

							snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
							return wi, wo





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']





def 		__UpperCAmelCase     (	a_				,	*, a_				,	a_):
							snake_case_           =				traverse_util.flatten_dict(variables['target'])
							snake_case_           =				{'/'.join(a_): v for k, v in old.items()}

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

							snake_case_           =				collections.OrderedDict()

							# Shared embeddings.
							snake_case_           =				old['token_embedder/embedding']

							# Encoder.
							for i in range(a_):
														# Block i, layer 0 (Self Attention).
														snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'encoder'				,	'pre_attention_layer_norm')
														snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'encoder'				,	'attention')
														snake_case_           =				layer_norm
														snake_case_           =				k.T
														snake_case_           =				o.T
														snake_case_           =				q.T
														snake_case_           =				v.T

														# Block i, layer 1 (MLP).
														snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'encoder'				,	'pre_mlp_layer_norm')
														snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'encoder'				,	a_)
														snake_case_           =				layer_norm
														if split_mlp_wi:
																					snake_case_           =				wi[0].T
																					snake_case_           =				wi[1].T
														else:
																					snake_case_           =				wi.T
														snake_case_           =				wo.T

							snake_case_           =				old[
							    'encoder/relpos_bias/rel_embedding'
							].T
							snake_case_           =				old['encoder/encoder_norm/scale']

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

																					# Block i, layer 1 (Cross Attention).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_cross_attention_layer_norm')
																					snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'decoder'				,	'encoder_decoder_attention')
																					snake_case_           =				layer_norm
																					snake_case_           =				k.T
																					snake_case_           =				o.T
																					snake_case_           =				q.T
																					snake_case_           =				v.T

																					# Block i, layer 2 (MLP).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_mlp_layer_norm')
																					snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'decoder'				,	a_)
																					snake_case_           =				layer_norm
																					if split_mlp_wi:
																												snake_case_           =				wi[0].T
																												snake_case_           =				wi[1].T
																					else:
																												snake_case_           =				wi.T
																					snake_case_           =				wo.T

														snake_case_           =				old['decoder/decoder_norm/scale']
														snake_case_           =				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:
																					snake_case_           =				old['decoder/logits_dense/kernel'].T

							return new





def 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				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:
														snake_case_           =				state_dict['shared.weight']

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

							return state_dict





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				checkpoints.load_tax_checkpoint(a_)
							snake_case_           =				convert_tax_to_pytorch(a_				,	num_layers=config.num_layers				,	is_encoder_only=a_)
							snake_case_           =				make_state_dict(a_				,	a_)
							model.load_state_dict(a_				,	strict=a_)





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = False):
							snake_case_           =				TaConfig.from_json_file(a_)
							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:
														snake_case_           =				TaEncoderModel(a_)
							else:
														snake_case_           =				TaForConditionalGeneration(a_)

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(a_)
							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
	)
	lowercase    				=   parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)





