




'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		int:
			A_       		=   TaConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
			A_       		=   FlaxTaForConditionalGeneration(config=SCREAMING_SNAKE_CASE_    )
			A_       		=   checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_    )

			A_       		=   'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

			# Encoder
			for layer_index in range(config.num_layers    ):
						A_       		=   F'layers_{str(SCREAMING_SNAKE_CASE_    )}'

						# Self-Attention
						A_       		=   tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
						A_       		=   tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
						A_       		=   tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
						A_       		=   tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

						# Layer Normalization
						A_       		=   tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

						if split_mlp_wi:
									A_       		=   tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
									A_       		=   tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									A_       		=   tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

						A_       		=   tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						A_       		=   tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						A_       		=   tax_attention_key
						A_       		=   tax_attention_out
						A_       		=   tax_attention_query
						A_       		=   tax_attention_value

						A_       		=   tax_attention_layer_norm

						if split_mlp_wi:
									A_       		=   tax_mlp_wi_a
									A_       		=   tax_mlp_wi_a
						else:
									A_       		=   tax_mlp_wi

						A_       		=   tax_mlp_wo
						A_       		=   tax_mlp_layer_norm

			# Only for layer 0:
			A_       		=   tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
			A_       		=   tax_encoder_rel_embedding

			# Assigning
			A_       		=   tax_model['target']['encoder']['encoder_norm']['scale']
			A_       		=   tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers    ):
						A_       		=   F'layers_{str(SCREAMING_SNAKE_CASE_    )}'

						# Self-Attention
						A_       		=   tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
						A_       		=   tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
						A_       		=   tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
						A_       		=   tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

						# Layer Normalization
						A_       		=   tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
						    'scale'
						]

						# Encoder-Decoder-Attention
						A_       		=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
						    'kernel'
						]
						A_       		=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
						    'kernel'
						]
						A_       		=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
						    'kernel'
						]
						A_       		=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
						    'kernel'
						]

						# Layer Normalization
						A_       		=   tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

						A_       		=   tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						A_       		=   tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						A_       		=   tax_attention_key
						A_       		=   tax_attention_out
						A_       		=   tax_attention_query
						A_       		=   tax_attention_value

						A_       		=   tax_pre_attention_layer_norm

						A_       		=   tax_enc_dec_attention_key
						A_       		=   tax_enc_dec_attention_out
						A_       		=   tax_enc_dec_attention_query
						A_       		=   tax_enc_dec_attention_value

						A_       		=   tax_cross_layer_norm

						if split_mlp_wi:
									A_       		=   tax_mlp_wi_a
									A_       		=   tax_mlp_wi_a
						else:
									A_       		=   tax_mlp_wi

						A_       		=   tax_mlp_wo

						A_       		=   txa_mlp_layer_norm

			# Decoder Normalization
			A_       		=   tax_model['target']['decoder']['decoder_norm']['scale']
			A_       		=   txa_decoder_norm

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

			# Token Embeddings
			A_       		=   tax_model['target']['token_embedder']['embedding']
			A_       		=   txa_token_embeddings

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

			flax_model.save_pretrained(SCREAMING_SNAKE_CASE_    )
			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 TX5 checkpoint."
							)
							parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of 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 unittest.mock import patch

import datasets
from datasets import Dataset





def  lowerCamelCase(       )		->		Any:
			A_       		=   Dataset.from_dict({'col_1': [3, 2, 0, 1]}    )

			with patch('tqdm.auto.tqdm'    ) as mock_tqdm:
						datasets.disable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_not_called()

						mock_tqdm.reset_mock()

						datasets.enable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_called()




