



"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

_lowercase				:				Any   		=		logging.get_logger(__name__)






def   snake_case__     (	__lowerCamelCase       :					int			):
			"""simple docstring"""
			if isinstance(__lowerCAmelCase							,	(list, tuple)			) and isinstance(videos[0]							,	(list, tuple)			) and is_valid_image(videos[0][0]			):
						return videos

			elif isinstance(__lowerCAmelCase							,	(list, tuple)			) and is_valid_image(videos[0]			):
						return [videos]

			elif is_valid_image(__lowerCAmelCase			):
						return [[videos]]

			raise ValueError(f'''Could not make batched video from {videos}'''			)

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							['pixel_values']



					def __init__( self							:						Optional[int],						lowerCamelCase							:						int = True,						lowerCamelCase							:						Optional[Any] = None,						lowerCamelCase							:						Union[str, Any] = PILImageResampling.BILINEAR,						lowerCamelCase							:						Dict = True,						lowerCamelCase							:						int = None,						lowerCamelCase							:						Dict = True,						lowerCamelCase							:						str = 1 / 255,						lowerCamelCase							:						Any = True,						lowerCamelCase							:						str = True,						lowerCamelCase							:						Optional[Any] = None,						lowerCamelCase							:						Tuple = None,						**lowerCamelCase							:						int,						)->      Tuple:
								super().__init__(**_lowerCAmelCase  )
								lowerCamelCase__	:	Dict				=size if size is not None else {'''shortest_edge''': 256}
								lowerCamelCase__	:	Dict				=get_size_dict(_lowerCAmelCase,						default_to_square=_lowerCAmelCase  )
								lowerCamelCase__	:	List[str]				=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowerCamelCase__	:	List[str]				=get_size_dict(_lowerCAmelCase,						param_name='''crop_size'''  )

								lowerCamelCase__	:	str				=do_resize
								lowerCamelCase__	:	Optional[int]				=size
								lowerCamelCase__	:	int				=do_center_crop
								lowerCamelCase__	:	Optional[int]				=crop_size
								lowerCamelCase__	:	Optional[Any]				=resample
								lowerCamelCase__	:	Dict				=do_rescale
								lowerCamelCase__	:	int				=rescale_factor
								lowerCamelCase__	:	Optional[int]				=offset
								lowerCamelCase__	:	Optional[Any]				=do_normalize
								lowerCamelCase__	:	int				=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCamelCase__	:	str				=image_std if image_std is not None else IMAGENET_STANDARD_STD



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Tuple = PILImageResampling.BILINEAR,						lowerCamelCase							:						Optional[int] = None,						**lowerCamelCase							:						int,						)->      List[Any]:
								lowerCamelCase__	:	Any				=get_size_dict(_lowerCAmelCase,						default_to_square=_lowerCAmelCase  )
								if "shortest_edge" in size:
											lowerCamelCase__	:	List[str]				=get_resize_output_image_size(_lowerCAmelCase,						size['''shortest_edge'''],						default_to_square=_lowerCAmelCase  )
								elif "height" in size and "width" in size:
											lowerCamelCase__	:	Dict				=(size['''height'''], size['''width'''])
								else:
											raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
								return resize(_lowerCAmelCase,						size=_lowerCAmelCase,						resample=_lowerCAmelCase,						data_format=_lowerCAmelCase,						**_lowerCAmelCase  )



					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Dict = None,						**lowerCamelCase							:						Dict,						)->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=get_size_dict(_lowerCAmelCase  )
								if "height" not in size or "width" not in size:
											raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
								return center_crop(_lowerCAmelCase,						size=(size['''height'''], size['''width''']),						data_format=_lowerCAmelCase,						**_lowerCAmelCase  )



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						int,						lowerCamelCase							:						List[str],						lowerCamelCase							:						Optional[Any] = True,						lowerCamelCase							:						int = None,						**lowerCamelCase							:						Optional[Any],						)->      Dict:
								lowerCamelCase__	:	List[str]				=image.astype(np.floataa  )
								if offset:
											lowerCamelCase__	:	List[str]				=image - (scale / 2)
								return rescale(_lowerCAmelCase,						scale=_lowerCAmelCase,						data_format=_lowerCAmelCase,						**_lowerCAmelCase  )



					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						List[Any],						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Optional[int] = None,						**lowerCamelCase							:						List[str],						)->      Optional[Any]:
								return normalize(_lowerCAmelCase,						mean=_lowerCAmelCase,						std=_lowerCAmelCase,						data_format=_lowerCAmelCase,						**_lowerCAmelCase  )



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Any = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						int = None,						lowerCamelCase							:						Optional[Any] = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						List[Any] = None,						lowerCamelCase							:						Tuple = None,						lowerCamelCase							:						Union[str, Any] = None,						lowerCamelCase							:						Union[str, Any] = ChannelDimension.FIRST,						)->      Any:
								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_center_crop and crop_size is None:
											raise ValueError('''Crop size must be specified if do_center_crop 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.'''  )

								if offset and not do_rescale:
											raise ValueError('''For offset, do_rescale must also be set to True.'''  )

								# All transformations expect numpy arrays.
								lowerCamelCase__	:	Dict				=to_numpy_array(_lowerCAmelCase  )

								if do_resize:
											lowerCamelCase__	:	str				=self.resize(image=_lowerCAmelCase,						size=_lowerCAmelCase,						resample=_lowerCAmelCase  )

								if do_center_crop:
											lowerCamelCase__	:	Tuple				=self.center_crop(_lowerCAmelCase,						size=_lowerCAmelCase  )

								if do_rescale:
											lowerCamelCase__	:	Optional[Any]				=self.rescale(image=_lowerCAmelCase,						scale=_lowerCAmelCase,						offset=_lowerCAmelCase  )

								if do_normalize:
											lowerCamelCase__	:	List[Any]				=self.normalize(image=_lowerCAmelCase,						mean=_lowerCAmelCase,						std=_lowerCAmelCase  )

								lowerCamelCase__	:	Union[str, Any]				=to_channel_dimension_format(_lowerCAmelCase,						_lowerCAmelCase  )
								return image




					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						List[Any] = None,						lowerCamelCase							:						Tuple = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						str = None,						lowerCamelCase							:						Any = None,						lowerCamelCase							:						Union[str, Any] = None,						lowerCamelCase							:						Tuple = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						List[str] = None,						lowerCamelCase							:						Tuple = None,						lowerCamelCase							:						int = None,						lowerCamelCase							:						Optional[Any] = ChannelDimension.FIRST,						**lowerCamelCase							:						List[Any],						)->      Tuple:
								lowerCamelCase__	:	Optional[int]				=do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__	:	Union[str, Any]				=resample if resample is not None else self.resample
								lowerCamelCase__	:	Tuple				=do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase__	:	Union[str, Any]				=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__	:	List[str]				=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__	:	Optional[Any]				=offset if offset is not None else self.offset
								lowerCamelCase__	:	Any				=do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__	:	Union[str, Any]				=image_mean if image_mean is not None else self.image_mean
								lowerCamelCase__	:	Any				=image_std if image_std is not None else self.image_std

								lowerCamelCase__	:	List[str]				=size if size is not None else self.size
								lowerCamelCase__	:	List[str]				=get_size_dict(_lowerCAmelCase,						default_to_square=_lowerCAmelCase  )
								lowerCamelCase__	:	Tuple				=crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__	:	Optional[int]				=get_size_dict(_lowerCAmelCase,						param_name='''crop_size'''  )

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

								lowerCamelCase__	:	Any				=make_batched(_lowerCAmelCase  )

								lowerCamelCase__	:	int				=[
								    [
								        self._preprocess_image(
								            image=_lowerCAmelCase,						do_resize=_lowerCAmelCase,						size=_lowerCAmelCase,						resample=_lowerCAmelCase,						do_center_crop=_lowerCAmelCase,						crop_size=_lowerCAmelCase,						do_rescale=_lowerCAmelCase,						rescale_factor=_lowerCAmelCase,						offset=_lowerCAmelCase,						do_normalize=_lowerCAmelCase,						image_mean=_lowerCAmelCase,						image_std=_lowerCAmelCase,						data_format=_lowerCAmelCase,						)
								        for img in video
								    ]
								    for video in videos
								]

								lowerCamelCase__	:	Tuple				={'''pixel_values''': videos}
								return BatchFeature(data=_lowerCAmelCase,						tensor_type=_lowerCAmelCase  )










"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=AutoConfig.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoints.load_tax_checkpoint(__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			if config.model_type == "t5":
						lowerCamelCase__	:	List[str]				='''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCamelCase__	:	List[Any]				='''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[Any]				='''TransientGlobalSelfAttention'''
			else:
						raise ValueError(
						    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
						    ''' attribute with a value from [\'local\', \'transient-global].'''			)

			# Encoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	List[Any]				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	str				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# Layer Normalization
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Dict				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Tuple				=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''encoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	int				=tax_attention_key
						lowerCamelCase__	:	Optional[int]				=tax_attention_out
						lowerCamelCase__	:	List[Any]				=tax_attention_query
						lowerCamelCase__	:	Optional[Any]				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	Optional[int]				=tax_global_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	Union[str, Any]				=tax_mlp_wi

						lowerCamelCase__	:	str				=tax_mlp_wo
						lowerCamelCase__	:	Optional[Any]				=tax_mlp_layer_norm

						lowerCamelCase__	:	Optional[int]				=flax_model_encoder_layer_block

			# Only for layer 0:
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	str				=tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
						lowerCamelCase__	:	Optional[int]				=tax_encoder_global_rel_embedding

			# Assigning
			lowerCamelCase__	:	int				=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			lowerCamelCase__	:	List[Any]				=tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	Dict				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_module['''key''']['''kernel''']
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_module['''out''']['''kernel''']
						lowerCamelCase__	:	Dict				=tax_enc_dec_attention_module['''query''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_enc_dec_attention_module['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Any				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''decoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	Union[str, Any]				=tax_attention_key
						lowerCamelCase__	:	str				=tax_attention_out
						lowerCamelCase__	:	Optional[int]				=tax_attention_query
						lowerCamelCase__	:	Dict				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_pre_attention_layer_norm

						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_key
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_out
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_query
						lowerCamelCase__	:	Optional[int]				=tax_enc_dec_attention_value

						lowerCamelCase__	:	Dict				=tax_cross_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Tuple				=tax_mlp_wi_a
									lowerCamelCase__	:	int				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	List[Any]				=tax_mlp_wi

						lowerCamelCase__	:	Dict				=tax_mlp_wo

						lowerCamelCase__	:	Tuple				=txa_mlp_layer_norm

						lowerCamelCase__	:	Optional[Any]				=flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCamelCase__	:	int				=txa_decoder_norm

			# Only for layer 0:
			lowerCamelCase__	:	Tuple				=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	Tuple				=tax_decoder_rel_embedding

			# Token Embeddings
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''token_embedder''']['''embedding''']
			lowerCamelCase__	:	Dict				=txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

			flax_model.save_pretrained(__lowerCamelCase			)
			print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
		_lowercase				:				Tuple   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
		)
		parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
		parser.add_argument(
		    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
		)
		_lowercase				:				List[Any]   		=		parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




