






import argparse
import json
import re
import string
import sys
from collections import Counter





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       List[str]:







			'''simple docstring'''

			def remove_articles(UpperCamelCase__				):
						return re.sub(R'''\b(a|an|the)\b'''    ,    ''' '''    ,    UpperCamelCase__				)

			def white_space_fix(UpperCamelCase__				):
						return " ".join(text.split()				)

			def remove_punc(UpperCamelCase__				):
						UpperCAmelCase     	=       set(string.punctuation				)
						return "".join(ch for ch in text if ch not in exclude				)

			def lower(UpperCamelCase__				):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__				)				)				)				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       List[str]:







			'''simple docstring'''

			UpperCAmelCase     	=       normalize_answer(UpperCamelCase__				).split()
			UpperCAmelCase     	=       normalize_answer(UpperCamelCase__				).split()
			UpperCAmelCase     	=       Counter(UpperCamelCase__				) & Counter(UpperCamelCase__				)
			UpperCAmelCase     	=       sum(common.values()				)
			if num_same == 0:
						return 0
			UpperCAmelCase     	=       1.0 * num_same / len(UpperCamelCase__				)
			UpperCAmelCase     	=       1.0 * num_same / len(UpperCamelCase__				)
			UpperCAmelCase     	=       (2 * precision * recall) / (precision + recall)
			return fa





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       List[str]:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__				) == normalize_answer(UpperCamelCase__				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Optional[int]:







			'''simple docstring'''

			UpperCAmelCase     	=       []
			for ground_truth in ground_truths:
						UpperCAmelCase     	=       metric_fn(UpperCamelCase__    ,    UpperCamelCase__				)
						scores_for_ground_truths.append(UpperCamelCase__				)
			return max(UpperCamelCase__				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Any:







			'''simple docstring'''

			UpperCAmelCase     	=       UpperCAmelCase     	=       UpperCAmelCase     	=       0
			for article in dataset:
						for paragraph in article["paragraphs"]:
									for qa in paragraph["qas"]:
												total += 1
												if qa["id"] not in predictions:
															UpperCAmelCase     	=       '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
															print(UpperCamelCase__    ,    file=sys.stderr				)
															continue
												UpperCAmelCase     	=       [x['''text'''] for x in qa['''answers''']]
												UpperCAmelCase     	=       predictions[qa['''id''']]
												exact_match += metric_max_over_ground_truths(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)
												fa += metric_max_over_ground_truths(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)

			UpperCAmelCase     	=       100.0 * exact_match / total
			UpperCAmelCase     	=       100.0 * fa / total

			return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
				__A       :					str     					=     "1.1"
				__A       :					Tuple     					=     argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__A       :					Dict     					=     parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__A       :					Any     					=     json.load(dataset_file)
								if dataset_json["version"] != expected_version:
												print(
												    "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
												    file=sys.stderr,
												)
								__A       :					List[str]     					=     dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__A       :					str     					=     json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))










import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       TaConfig.from_pretrained(UpperCamelCase__				)
			UpperCAmelCase     	=       FlaxTaForConditionalGeneration(config=UpperCamelCase__				)
			UpperCAmelCase     	=       checkpoints.load_tax_checkpoint(UpperCamelCase__				)

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

			# Encoder
			for layer_index in range(config.num_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

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

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

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

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

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

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_attention_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo
						UpperCAmelCase     	=       tax_mlp_layer_norm

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

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

			# Decoder
			for layer_index in range(config.num_decoder_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

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

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

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

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

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

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

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

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_pre_attention_layer_norm

						UpperCAmelCase     	=       tax_enc_dec_attention_key
						UpperCAmelCase     	=       tax_enc_dec_attention_out
						UpperCAmelCase     	=       tax_enc_dec_attention_query
						UpperCAmelCase     	=       tax_enc_dec_attention_value

						UpperCAmelCase     	=       tax_cross_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo

						UpperCAmelCase     	=       txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
				__A       :					Union[str, Any]     					=     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."
				)
				__A       :					Any     					=     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



