






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Dict				):
			__a						:  Tuple									=     AutoConfig.from_pretrained(lowerCamelCase_				)
			__a						:  Optional[int]									=     FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_				)
			__a						:  Union[str, Any]									=     checkpoints.load_tax_checkpoint(lowerCamelCase_				)

			__a						:  str									=     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

			if config.model_type == "t5":
						__a						:  Dict									=     'SelfAttention'
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__a						:  int									=     'LocalSelfAttention'
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a						:  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				):
						__a						:  List[Any]									=     f'''layers_{str(lowerCamelCase_				)}'''

						# Self-Attention
						__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
						__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
						__a						:  int									=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
						__a						:  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":
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

						# Layer Normalization
						__a						:  Union[str, Any]									=     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

						if split_mlp_wi:
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
									__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

						__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__a						:  Optional[Any]									=     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__a						:  int									=     flax_model.params['encoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  str									=     tax_attention_key
						__a						:  Dict									=     tax_attention_out
						__a						:  Union[str, Any]									=     tax_attention_query
						__a						:  List[str]									=     tax_attention_value

						__a						:  str									=     tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__a						:  Any									=     tax_global_layer_norm

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

						__a						:  List[Any]									=     tax_mlp_wo
						__a						:  List[Any]									=     tax_mlp_layer_norm

						__a						:  Optional[Any]									=     flax_model_encoder_layer_block

			# Only for layer 0:
			__a						:  Dict									=     tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[int]									=     tax_encoder_rel_embedding

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

			# Assigning
			__a						:  List[str]									=     tax_model['target']['encoder']['encoder_norm']['scale']
			__a						:  Tuple									=     tax_encoder_norm

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

						# Self-Attention
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
						__a						:  List[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
						__a						:  Optional[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

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

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

						# Layer Normalization
						__a						:  int									=     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

						# MLP
						if split_mlp_wi:
									__a						:  Tuple									=     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
									__a						:  Tuple									=     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__a						:  Dict									=     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

						__a						:  int									=     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

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

						# Assigning
						__a						:  Union[str, Any]									=     flax_model.params['decoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  List[str]									=     tax_attention_key
						__a						:  Any									=     tax_attention_out
						__a						:  List[Any]									=     tax_attention_query
						__a						:  Optional[Any]									=     tax_attention_value

						__a						:  Any									=     tax_pre_attention_layer_norm

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

						__a						:  Dict									=     tax_cross_layer_norm

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

						__a						:  Dict									=     tax_mlp_wo

						__a						:  Union[str, Any]									=     txa_mlp_layer_norm

						__a						:  Optional[int]									=     flax_model_decoder_layer_block

			# Decoder Normalization
			__a						:  Tuple									=     tax_model['target']['decoder']['decoder_norm']['scale']
			__a						:  Optional[Any]									=     txa_decoder_norm

			# Only for layer 0:
			__a						:  Optional[int]									=     tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[Any]									=     tax_decoder_rel_embedding

			# Token Embeddings
			__a						:  Tuple									=     tax_model['target']['token_embedder']['embedding']
			__a						:  List[Any]									=     txa_token_embeddings

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			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.'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












"""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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


a		:					int	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Tuple      =							do_resize
								a :						int      =							size
								a :						List[str]      =							resample
								a :						List[Any]      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Union[str, Any]      =							do_rescale
								a :						List[Any]      =							rescale_factor
								a :						Union[str, Any]      =							do_normalize
								a :						Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return resize(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return center_crop(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__=None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								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." )

								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." )

								# All transformations expect numpy arrays.
								a :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

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

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


