




"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    str					=					AutoConfig.from_pretrained(lowercase__	)
				_lowerCamelCase  :    Any					=					FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase__	)
				_lowerCamelCase  :    Optional[Any]					=					checkpoints.load_tax_checkpoint(lowercase__	)

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

				if config.model_type == "t5":
								_lowerCamelCase  :    Optional[int]					=					'SelfAttention'
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								_lowerCamelCase  :    str					=					'LocalSelfAttention'
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								_lowerCamelCase  :    Union[str, 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  :    Union[str, Any]					=					f'''layers_{str(lowercase__	)}'''

								# Self-Attention
								_lowerCamelCase  :    Union[str, Any]					=					tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
								_lowerCamelCase  :    Any					=					tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
								_lowerCamelCase  :    Any					=					tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
								_lowerCamelCase  :    Optional[int]					=					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  :    List[str]					=					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

								# Layer Normalization
								_lowerCamelCase  :    Optional[int]					=					tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

								if split_mlp_wi:
												_lowerCamelCase  :    Tuple					=					tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
												_lowerCamelCase  :    Any					=					tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
								else:
												_lowerCamelCase  :    str					=					tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

								_lowerCamelCase  :    Tuple					=					tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

								# Layer Normalization
								_lowerCamelCase  :    Optional[int]					=					tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

								_lowerCamelCase  :    str					=					tax_attention_layer_norm

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

								if split_mlp_wi:
												_lowerCamelCase  :    Dict					=					tax_mlp_wi_a
												_lowerCamelCase  :    int					=					tax_mlp_wi_a
								else:
												_lowerCamelCase  :    Optional[Any]					=					tax_mlp_wi

								_lowerCamelCase  :    Any					=					tax_mlp_wo
								_lowerCamelCase  :    Union[str, Any]					=					tax_mlp_layer_norm

								_lowerCamelCase  :    int					=					flax_model_encoder_layer_block

				# Only for layer 0:
				_lowerCamelCase  :    Dict					=					tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
				_lowerCamelCase  :    List[Any]					=					tax_encoder_rel_embedding

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

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

				# Decoder
				for layer_index in range(config.num_layers	):
								_lowerCamelCase  :    Tuple					=					f'''layers_{str(lowercase__	)}'''

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

								# Layer Normalization
								_lowerCamelCase  :    Tuple					=					tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
								    'scale'
								]

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

								# Layer Normalization
								_lowerCamelCase  :    Union[str, Any]					=					tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

								# MLP
								if split_mlp_wi:
												_lowerCamelCase  :    Optional[Any]					=					tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
												_lowerCamelCase  :    int					=					tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
								else:
												_lowerCamelCase  :    int					=					tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

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

								# Layer Normalization
								_lowerCamelCase  :    Any					=					tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

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

								_lowerCamelCase  :    int					=					tax_pre_attention_layer_norm

								_lowerCamelCase  :    Optional[Any]					=					tax_enc_dec_attention_key
								_lowerCamelCase  :    Dict					=					tax_enc_dec_attention_out
								_lowerCamelCase  :    Optional[int]					=					tax_enc_dec_attention_query
								_lowerCamelCase  :    Any					=					tax_enc_dec_attention_value

								_lowerCamelCase  :    str					=					tax_cross_layer_norm

								if split_mlp_wi:
												_lowerCamelCase  :    int					=					tax_mlp_wi_a
												_lowerCamelCase  :    Optional[int]					=					tax_mlp_wi_a
								else:
												_lowerCamelCase  :    List[str]					=					tax_mlp_wi

								_lowerCamelCase  :    Optional[Any]					=					tax_mlp_wo

								_lowerCamelCase  :    Union[str, Any]					=					txa_mlp_layer_norm

								_lowerCamelCase  :    Dict					=					flax_model_decoder_layer_block

				# Decoder Normalization
				_lowerCamelCase  :    Union[str, Any]					=					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  :    str					=					tax_decoder_rel_embedding

				# Token Embeddings
				_lowerCamelCase  :    List[str]					=					tax_model['target']['token_embedder']['embedding']
				_lowerCamelCase  :    Optional[int]					=					txa_token_embeddings

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

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


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





"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()