
'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						_SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

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

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

												# Layer Normalization
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

												_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_SCREAMING_SNAKE_CASE				= flax_model.params["encoder"]["block"][str(_SCREAMING_SNAKE_CASE  )]["layer"]
												_SCREAMING_SNAKE_CASE				= tax_attention_key
												_SCREAMING_SNAKE_CASE				= tax_attention_out
												_SCREAMING_SNAKE_CASE				= tax_attention_query
												_SCREAMING_SNAKE_CASE				= tax_attention_value

												_SCREAMING_SNAKE_CASE				= tax_attention_layer_norm

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

												if split_mlp_wi:
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi_a
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi_a
												else:
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi

												_SCREAMING_SNAKE_CASE				= tax_mlp_wo
												_SCREAMING_SNAKE_CASE				= tax_mlp_layer_norm

												_SCREAMING_SNAKE_CASE				= flax_model_encoder_layer_block

						# Only for layer 0:
						_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
						_SCREAMING_SNAKE_CASE				= tax_encoder_rel_embedding

						# Side/global relative position_bias + layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
												_SCREAMING_SNAKE_CASE				= tax_encoder_global_rel_embedding

						# Assigning
						_SCREAMING_SNAKE_CASE				= tax_model["target"]["encoder"]["encoder_norm"]["scale"]
						_SCREAMING_SNAKE_CASE				= tax_encoder_norm

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

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

												# Layer Normalization
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
												    "scale"
												]

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

												# Layer Normalization
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

												_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_SCREAMING_SNAKE_CASE				= flax_model.params["decoder"]["block"][str(_SCREAMING_SNAKE_CASE  )]["layer"]
												_SCREAMING_SNAKE_CASE				= tax_attention_key
												_SCREAMING_SNAKE_CASE				= tax_attention_out
												_SCREAMING_SNAKE_CASE				= tax_attention_query
												_SCREAMING_SNAKE_CASE				= tax_attention_value

												_SCREAMING_SNAKE_CASE				= tax_pre_attention_layer_norm

												_SCREAMING_SNAKE_CASE				= tax_enc_dec_attention_key
												_SCREAMING_SNAKE_CASE				= tax_enc_dec_attention_out
												_SCREAMING_SNAKE_CASE				= tax_enc_dec_attention_query
												_SCREAMING_SNAKE_CASE				= tax_enc_dec_attention_value

												_SCREAMING_SNAKE_CASE				= tax_cross_layer_norm

												if split_mlp_wi:
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi_a
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi_a
												else:
																		_SCREAMING_SNAKE_CASE				= tax_mlp_wi

												_SCREAMING_SNAKE_CASE				= tax_mlp_wo

												_SCREAMING_SNAKE_CASE				= txa_mlp_layer_norm

												_SCREAMING_SNAKE_CASE				= flax_model_decoder_layer_block

						# Decoder Normalization
						_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"]["decoder_norm"]["scale"]
						_SCREAMING_SNAKE_CASE				= txa_decoder_norm

						# Only for layer 0:
						_SCREAMING_SNAKE_CASE				= tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
						_SCREAMING_SNAKE_CASE				= tax_decoder_rel_embedding

						# Token Embeddings
						_SCREAMING_SNAKE_CASE				= tax_model["target"]["token_embedder"]["embedding"]
						_SCREAMING_SNAKE_CASE				= txa_token_embeddings

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

						flax_model.save_pretrained(_SCREAMING_SNAKE_CASE  )
						print("T5X Model was sucessfully converted!"  )


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

'''simple docstring'''







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					int       =					LayoutLMTokenizer
		a							:					Optional[int]       =					LayoutLMTokenizerFast
		a							:					Optional[int]       =					True
		a							:					Any       =					True




		def     lowercase       (				self      ):
								super().setUp()

								_SCREAMING_SNAKE_CASE				= [
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file					,						"w"					,						encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return LayoutLMTokenizer.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "UNwant\u00E9d,running"
								_SCREAMING_SNAKE_CASE				= "unwanted, running"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("UNwant\u00E9d,running"      )
								self.assertListEqual(UpperCamelCase					,						["un", "##want", "##ed", ",", "runn", "##ing"]      )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase      )					,						[7, 4, 5, 10, 8, 9]      )




		def     lowercase       (				self      ):
								pass