






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Dict				):
			__a						:  Tuple									=     AutoConfig.from_pretrained(lowerCamelCase_				)
			__a						:  Optional[int]									=     FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_				)
			__a						:  Union[str, Any]									=     checkpoints.load_tax_checkpoint(lowerCamelCase_				)

			__a						:  str									=     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

			if config.model_type == "t5":
						__a						:  Dict									=     'SelfAttention'
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__a						:  int									=     'LocalSelfAttention'
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a						:  Any									=     '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				):
						__a						:  List[Any]									=     f'''layers_{str(lowerCamelCase_				)}'''

						# Self-Attention
						__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
						__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
						__a						:  int									=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
						__a						:  List[Any]									=     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":
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

						# Layer Normalization
						__a						:  Union[str, Any]									=     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

						if split_mlp_wi:
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
									__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

						__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__a						:  Optional[Any]									=     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__a						:  int									=     flax_model.params['encoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  str									=     tax_attention_key
						__a						:  Dict									=     tax_attention_out
						__a						:  Union[str, Any]									=     tax_attention_query
						__a						:  List[str]									=     tax_attention_value

						__a						:  str									=     tax_attention_layer_norm

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

						if split_mlp_wi:
									__a						:  Any									=     tax_mlp_wi_a
									__a						:  Optional[int]									=     tax_mlp_wi_a
						else:
									__a						:  Union[str, Any]									=     tax_mlp_wi

						__a						:  List[Any]									=     tax_mlp_wo
						__a						:  List[Any]									=     tax_mlp_layer_norm

						__a						:  Optional[Any]									=     flax_model_encoder_layer_block

			# Only for layer 0:
			__a						:  Dict									=     tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[int]									=     tax_encoder_rel_embedding

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

			# Assigning
			__a						:  List[str]									=     tax_model['target']['encoder']['encoder_norm']['scale']
			__a						:  Tuple									=     tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers				):
						__a						:  Dict									=     f'''layers_{str(lowerCamelCase_				)}'''

						# Self-Attention
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
						__a						:  List[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
						__a						:  Optional[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

						# Layer Normalization
						__a						:  int									=     tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
						    'scale'
						]

						# Encoder-Decoder-Attention
						__a						:  str									=     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
						__a						:  Union[str, Any]									=     tax_enc_dec_attention_module['key']['kernel']
						__a						:  List[Any]									=     tax_enc_dec_attention_module['out']['kernel']
						__a						:  Optional[int]									=     tax_enc_dec_attention_module['query']['kernel']
						__a						:  List[Any]									=     tax_enc_dec_attention_module['value']['kernel']

						# Layer Normalization
						__a						:  int									=     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

						__a						:  int									=     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__a						:  List[str]									=     tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__a						:  Union[str, Any]									=     flax_model.params['decoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  List[str]									=     tax_attention_key
						__a						:  Any									=     tax_attention_out
						__a						:  List[Any]									=     tax_attention_query
						__a						:  Optional[Any]									=     tax_attention_value

						__a						:  Any									=     tax_pre_attention_layer_norm

						__a						:  int									=     tax_enc_dec_attention_key
						__a						:  Optional[Any]									=     tax_enc_dec_attention_out
						__a						:  List[str]									=     tax_enc_dec_attention_query
						__a						:  str									=     tax_enc_dec_attention_value

						__a						:  Dict									=     tax_cross_layer_norm

						if split_mlp_wi:
									__a						:  Union[str, Any]									=     tax_mlp_wi_a
									__a						:  int									=     tax_mlp_wi_a
						else:
									__a						:  int									=     tax_mlp_wi

						__a						:  Dict									=     tax_mlp_wo

						__a						:  Union[str, Any]									=     txa_mlp_layer_norm

						__a						:  Optional[int]									=     flax_model_decoder_layer_block

			# Decoder Normalization
			__a						:  Tuple									=     tax_model['target']['decoder']['decoder_norm']['scale']
			__a						:  Optional[Any]									=     txa_decoder_norm

			# Only for layer 0:
			__a						:  Optional[int]									=     tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[Any]									=     tax_decoder_rel_embedding

			# Token Embeddings
			__a						:  Tuple									=     tax_model['target']['token_embedder']['embedding']
			__a						:  List[Any]									=     txa_token_embeddings

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

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


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












'''simple docstring'''

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	int									=       OpenAIGPTTokenizer
       _snake_case      :	Tuple									=       OpenAIGPTTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _UpperCamelCase							       =			[
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''w</w>''',
                '''r</w>''',
                '''t</w>''',
                '''lo''',
                '''low''',
                '''er</w>''',
                '''low</w>''',
                '''lowest</w>''',
                '''newer</w>''',
                '''wider</w>''',
                '''<unk>''',
            ]
            _UpperCamelCase							       =			dict(zip(lowerCAmelCase__			,					range(len(lowerCAmelCase__	)	)	)	)
            _UpperCamelCase							       =			['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''vocab_file''']	)
            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''merges_file''']	)
            with open(self.vocab_file			,					'''w'''	) as fp:
                 fp.write(json.dumps(lowerCAmelCase__	)	)
            with open(self.merges_file			,					'''w'''	) as fp:
                 fp.write('''\n'''.join(lowerCAmelCase__	)	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            return "lower newer", "lower newer"





       def snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OpenAIGPTTokenizer(self.vocab_file			,					self.merges_file	)

            _UpperCamelCase							       =			'''lower'''
            _UpperCamelCase							       =			['''low''', '''er</w>''']
            _UpperCamelCase							       =			tokenizer.tokenize(lowerCAmelCase__	)
            self.assertListEqual(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			tokens + ['''<unk>''']
            _UpperCamelCase							       =			[14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Tuple=15	)       ->	Union[str, Any]:

            '''simple docstring'''




            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                      _UpperCamelCase							       =			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)

                      # Simple input
                      _UpperCamelCase							       =			'''This is a simple input'''
                      _UpperCamelCase							       =			['''This is a simple input 1''', '''This is a simple input 2''']
                      _UpperCamelCase							       =			('''This is a simple input''', '''This is a pair''')
                      _UpperCamelCase							       =			[
                          ('''This is a simple input 1''', '''This is a simple input 2'''),
                          ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                      ]

                      # Simple input tests
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)







       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            pass

@require_ftfy
@require_spacy
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       pass



