





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





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







			'''simple docstring'''

			UpperCAmelCase   		=				AutoConfig.from_pretrained(UpperCamelCase__ )
			UpperCAmelCase   		=				FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__ )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

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

			if config.model_type == "t5":
						UpperCAmelCase   		=				'''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						UpperCAmelCase   		=				'''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						UpperCAmelCase   		=				'''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 ):
						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''']

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									UpperCAmelCase   		=				tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# 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   		=				flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__ )]['''layer''']
						UpperCAmelCase   		=				tax_attention_key
						UpperCAmelCase   		=				tax_attention_out
						UpperCAmelCase   		=				tax_attention_query
						UpperCAmelCase   		=				tax_attention_value

						UpperCAmelCase   		=				tax_attention_layer_norm

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

						UpperCAmelCase   		=				flax_model_encoder_layer_block

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

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

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

			# Decoder
			for layer_index in range(config.num_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''']
						UpperCAmelCase   		=				tax_enc_dec_attention_module['''key''']['''kernel''']
						UpperCAmelCase   		=				tax_enc_dec_attention_module['''out''']['''kernel''']
						UpperCAmelCase   		=				tax_enc_dec_attention_module['''query''']['''kernel''']
						UpperCAmelCase   		=				tax_enc_dec_attention_module['''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   		=				flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__ )]['''layer''']
						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

						UpperCAmelCase   		=				flax_model_decoder_layer_block

			# 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 and LongT5 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				:    Any       =   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				:    Dict       =   parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



