
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 							a		(				snake_case__:   Dict		,  snake_case__:   Any		,  snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   AutoConfig.from_pretrained(snake_case__   )
				lowercase_	    =   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__   )
				lowercase_	    =   checkpoints.load_tax_checkpoint(snake_case__   )

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

				if config.model_type == "t5":
								lowercase_	    =   '''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								lowercase_	    =   '''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase_	    =   '''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   ):
								lowercase_	    =   F'''layers_{str(snake_case__   )}'''

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

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

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

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

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

								# Assigning
								lowercase_	    =   flax_model.params['''encoder''']['''block'''][str(snake_case__   )]['''layer''']
								lowercase_	    =   tax_attention_key
								lowercase_	    =   tax_attention_out
								lowercase_	    =   tax_attention_query
								lowercase_	    =   tax_attention_value

								lowercase_	    =   tax_attention_layer_norm

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

								if split_mlp_wi:
												lowercase_	    =   tax_mlp_wi_a
												lowercase_	    =   tax_mlp_wi_a
								else:
												lowercase_	    =   tax_mlp_wi

								lowercase_	    =   tax_mlp_wo
								lowercase_	    =   tax_mlp_layer_norm

								lowercase_	    =   flax_model_encoder_layer_block

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

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

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

				# Decoder
				for layer_index in range(config.num_layers   ):
								lowercase_	    =   F'''layers_{str(snake_case__   )}'''

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

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

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

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

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

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

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

								# Assigning
								lowercase_	    =   flax_model.params['''decoder''']['''block'''][str(snake_case__   )]['''layer''']
								lowercase_	    =   tax_attention_key
								lowercase_	    =   tax_attention_out
								lowercase_	    =   tax_attention_query
								lowercase_	    =   tax_attention_value

								lowercase_	    =   tax_pre_attention_layer_norm

								lowercase_	    =   tax_enc_dec_attention_key
								lowercase_	    =   tax_enc_dec_attention_out
								lowercase_	    =   tax_enc_dec_attention_query
								lowercase_	    =   tax_enc_dec_attention_value

								lowercase_	    =   tax_cross_layer_norm

								if split_mlp_wi:
												lowercase_	    =   tax_mlp_wi_a
												lowercase_	    =   tax_mlp_wi_a
								else:
												lowercase_	    =   tax_mlp_wi

								lowercase_	    =   tax_mlp_wo

								lowercase_	    =   txa_mlp_layer_norm

								lowercase_	    =   flax_model_decoder_layer_block

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

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

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

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

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


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








'''simple docstring'''
lowerCAmelCase   : List[str]             =				2_5_6
# Modulus to hash a string
lowerCAmelCase   : Tuple             =				1_0_0_0_0_0_3




def 	_A  (       A							,A							)  ->				bool:
	lowercase						:		List[Any]   					=		len(A							)
	lowercase						:		List[Any]   					=		len(A							)
	if p_len > t_len:
		return False

	lowercase						:		List[str]   					=		0
	lowercase						:		Dict   					=		0
	lowercase						:		Any   					=		1

	# Calculating the hash of pattern and substring of text
	for i in range(A							):
		lowercase						:		Optional[int]   					=		(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
		lowercase						:		Any   					=		(ord(text[i]							) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		lowercase						:		List[str]   					=		(modulus_power * alphabet_size) % modulus

	for i in range(0							,t_len - p_len + 1							):
		if text_hash == p_hash and text[i : i + p_len] == pattern:
			return True
		if i == t_len - p_len:
			continue
		# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
		lowercase						:		Any   					=		(
		    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]							)
		) % modulus
	return False




def 	_A  (       )  ->				None:
	lowercase						:		Dict   					=		"abc1abc12"
	lowercase						:		Union[str, Any]   					=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
	lowercase						:		Any   					=		"alskfjaldsk23adsfabcabc"
	assert rabin_karp(A							,A							) and not rabin_karp(A							,A							)

	# Test 2)
	lowercase						:		str   					=		"ABABX"
	lowercase						:		Union[str, Any]   					=		"ABABZABABYABABX"
	assert rabin_karp(A							,A							)

	# Test 3)
	lowercase						:		str   					=		"AAAB"
	lowercase						:		List[str]   					=		"ABAAAAAB"
	assert rabin_karp(A							,A							)

	# Test 4)
	lowercase						:		List[str]   					=		"abcdabcy"
	lowercase						:		str   					=		"abcxabcdabxabcdabcdabcy"
	assert rabin_karp(A							,A							)

	# Test 5)
	lowercase						:		int   					=		"Lü"
	lowercase						:		Optional[Any]   					=		"Lüsai"
	assert rabin_karp(A							,A							)
	lowercase						:		Tuple   					=		"Lue"
	assert not rabin_karp(A							,A							)
	print("Success."							)


if __name__ == "__main__":
					test_rabin_karp()


