




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_		:					Optional[int]          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""pixel_values"""]

						def __init__(		self :Optional[int]     ,			__snake_case :bool = True     ,			__snake_case :Union[int, float] = 1 / 2_55     ,			__snake_case :bool = True     ,			__snake_case :int = 8     ,			**__snake_case :int     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										__magic_name__						:						Optional[Any]										=do_rescale
										__magic_name__						:						List[Any]										=rescale_factor
										__magic_name__						:						Dict										=do_pad
										__magic_name__						:						Tuple										=pad_size

						def      A__    (		self :List[str]     ,			__snake_case :np.ndarray     ,			__snake_case :float     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None     ,			**__snake_case :Tuple			):
										'''simple docstring'''

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

						def      A__    (		self :List[Any]     ,			__snake_case :np.ndarray     ,			__snake_case :int     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Optional[int]										=get_image_size(__snake_case			)
										__magic_name__						:						List[Any]										=(old_height // size + 1) * size - old_height
										__magic_name__						:						Union[str, Any]										=(old_width // size + 1) * size - old_width

										return pad(__snake_case     ,			((0, pad_height), (0, pad_width))     ,			mode="""symmetric"""     ,			data_format=__snake_case			)


						def      A__    (		self :Union[str, Any]     ,			__snake_case :ImageInput     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[float] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			__snake_case :Union[str, ChannelDimension] = ChannelDimension.FIRST     ,			**__snake_case :Tuple     ,			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__						:						str										=rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__						:						Dict										=do_pad if do_pad is not None else self.do_pad
										__magic_name__						:						Union[str, Any]										=pad_size if pad_size is not None else self.pad_size

										__magic_name__						:						int										=make_list_of_images(__snake_case			)

										if not valid_images(__snake_case			):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

										if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

										# All transformations expect numpy arrays.
										__magic_name__						:						Optional[int]										=[to_numpy_array(__snake_case			) for image in images]

										if do_rescale:
														__magic_name__						:						Any										=[self.rescale(image=__snake_case     ,			scale=__snake_case			) for image in images]

										if do_pad:
														__magic_name__						:						Optional[Any]										=[self.pad(__snake_case     ,			size=__snake_case			) for image in images]

										__magic_name__						:						Any										=[to_channel_dimension_format(__snake_case     ,			__snake_case			) for image in images]

										__magic_name__						:						Dict										={"""pixel_values""": images}
										return BatchFeature(data=__snake_case     ,			tensor_type=__snake_case			)







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					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Optional[int]      )     ->	Dict:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[int]						,__a :     int						,__a :     List[str]="attention"      )     ->	List[str]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     Union[str, Any]						,__a :     List[Any]						,__a :     Any=False      )     ->	Any:

					"""simple docstring"""

					if split_mlp_wi:
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										_a      :       Union[str, Any]   =						params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										_a      :       List[str]   =						(wi_a, wi_a)
					else:
										_a      :       List[str]   =						params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

					_a      :       Optional[int]   =						params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
					return wi, wo



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Optional[Any]						,__a :     Union[str, Any]						,__a :     str      )     ->	List[str]:

					"""simple docstring"""

					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def   __UpperCAmelCase					(      __a :     dict						,*, __a :     int						,__a :     bool						,__a :     bool = False      )     ->	Any:

					"""simple docstring"""

					_a      :       Dict   =						traverse_util.flatten_dict(variables['''target''']      )
					_a      :       Any   =						{'''/'''.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
					_a      :       Optional[int]   =						'''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''						,__a      )

					_a      :       Tuple   =						collections.OrderedDict()

					# Shared embeddings.
					_a      :       Any   =						old['''token_embedder/embedding''']

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

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

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

					if not scalable_attention:
										_a      :       List[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''encoder'''      ).T
										_a      :       Optional[Any]   =						tax_relpos_bias_lookup(
										    __a						,0						,'''decoder'''      ).T

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

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

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

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_a      :       Tuple   =						tax_relpos_bias_lookup(__a						,__a						,'''decoder'''      ).T

										_a      :       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:
															_a      :       Any   =						old['''decoder/logits_dense/kernel'''].T

					return new



def   __UpperCAmelCase					(      __a :     Dict						,__a :     bool      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Tuple   =						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:
										_a      :       Any   =						state_dict['''shared.weight''']

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

					return state_dict



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Dict						,__a :     Union[str, Any]						,__a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       List[str]   =						checkpoints.load_tax_checkpoint(__a      )
					_a      :       str   =						convert_tax_to_pytorch(
					    __a						,num_layers=config.num_layers						,is_encoder_only=__a						,scalable_attention=__a      )
					_a      :       str   =						make_state_dict(__a						,__a      )
					model.load_state_dict(__a						,strict=__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Any						,__a :     Union[str, Any]						,__a :     bool = False						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						MTaConfig.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:
										_a      :       Any   =						UMTaEncoderModel(__a      )
					else:
										_a      :       Tuple   =						UMTaForConditionalGeneration(__a      )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(__a						,__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__":
			a__												=						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,
			)
			a__												=						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,
			)



