





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   checkpoints.load_tax_checkpoint(UpperCamelCase__     )

		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(UpperCamelCase__     )}'''

				# 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(UpperCamelCase__     )]["""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(UpperCamelCase__     )}'''

				# 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(UpperCamelCase__     )]["""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(UpperCamelCase__     )
		print("""T5X Model was sucessfully converted!"""     )


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

import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     ProphetNetTokenizer
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  Optional[Any] ):
												super().setUp()

												lowerCamelCase_					=       [
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												lowerCamelCase_					=       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       :  int ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       """UNwant\u00E9d,running"""
												lowerCamelCase_					=       """unwanted, running"""
												return input_text, output_text

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.tokenizer_class(self.vocab_file )

												lowerCamelCase_					=       tokenizer.tokenize("""UNwant\u00E9d,running""" )
												self.assertListEqual(__UpperCamelCase ,       ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) ,       [9, 6, 7, 1_2, 1_0, 1_1] )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BasicTokenizer()

												self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) ,       ["""ah""", """\u535A""", """\u63A8""", """zz"""] )

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ ) ,       ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""h\u00E9llo"""] )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       never_split=["""[UNK]"""] )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]""" ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

												lowerCamelCase_					=       {}
												for i, token in enumerate(__UpperCamelCase ):
																			lowerCamelCase_					=       i
												lowerCamelCase_					=       WordpieceTokenizer(vocab=__UpperCamelCase ,       unk_token="""[UNK]""" )

												self.assertListEqual(tokenizer.tokenize("""""" ) ,       [] )

												self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) ,       ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )

												self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) ,       ["""[UNK]""", """runn""", """##ing"""] )

					@require_torch
					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )

												lowerCamelCase_					=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												lowerCamelCase_					=       [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
												lowerCamelCase_					=       tokenizer(__UpperCamelCase ,       padding=__UpperCamelCase ,       return_tensors="""pt""" )
												self.assertIsInstance(__UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       list(batch.input_ids.numpy()[0] )
												self.assertListEqual(__UpperCamelCase ,       __UpperCamelCase )

												self.assertEqual((2, 9) ,       batch.input_ids.shape )
												self.assertEqual((2, 9) ,       batch.attention_mask.shape )

					def  lowercase__   (						self       :  List[str] ):
												self.assertTrue(_is_whitespace(""" """ ) )
												self.assertTrue(_is_whitespace("""\t""" ) )
												self.assertTrue(_is_whitespace("""\r""" ) )
												self.assertTrue(_is_whitespace("""\n""" ) )
												self.assertTrue(_is_whitespace("""\u00A0""" ) )

												self.assertFalse(_is_whitespace("""A""" ) )
												self.assertFalse(_is_whitespace("""-""" ) )

					def  lowercase__   (						self       :  Union[str, Any] ):
												self.assertTrue(_is_control("""\u0005""" ) )

												self.assertFalse(_is_control("""A""" ) )
												self.assertFalse(_is_control(""" """ ) )
												self.assertFalse(_is_control("""\t""" ) )
												self.assertFalse(_is_control("""\r""" ) )

					def  lowercase__   (						self       :  Tuple ):
												self.assertTrue(_is_punctuation("""-""" ) )
												self.assertTrue(_is_punctuation("""$""" ) )
												self.assertTrue(_is_punctuation("""`""" ) )
												self.assertTrue(_is_punctuation(""".""" ) )

												self.assertFalse(_is_punctuation("""A""" ) )
												self.assertFalse(_is_punctuation(""" """ ) )



					@slow
					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )

												lowerCamelCase_					=       tokenizer.encode("""sequence builders""" ,       add_special_tokens=__UpperCamelCase )
												lowerCamelCase_					=       tokenizer.encode("""multi-sequence build""" ,       add_special_tokens=__UpperCamelCase )

												lowerCamelCase_					=       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase )
												lowerCamelCase_					=       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase ,       __UpperCamelCase )

												assert encoded_sentence == text + [1_0_2]
												assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]





