
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]					):


				'''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)







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  Optional[Any]   	=						get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     XGLMTokenizer
			_UpperCAmelCase   :							List[Any]										=     XGLMTokenizerFast
			_UpperCAmelCase   :							Optional[int]										=     True
			_UpperCAmelCase   :							Tuple										=     True



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_: List[Any]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)
									tokenizer.save_pretrained(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: int  		=       1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__)   ,						lowerCAmelCase__)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__)   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]   ,						"<s>")
									self.assertEqual(vocab_keys[1]   ,						"<pad>")
									self.assertEqual(len(lowerCAmelCase__)   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									self.assertEqual(self.get_tokenizer().vocab_size   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__   ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)   ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,						)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]   ,						)



			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									return XGLMTokenizer.from_pretrained("facebook/xglm-564M")



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(lowerCAmelCase__   ,						f.name)
															SCREAMING_SNAKE_CASE_: Tuple  		=       XGLMTokenizer(f.name   ,						keep_accents=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[str]  		=       pickle.dumps(lowerCAmelCase__)
									pickle.loads(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									if not self.test_rust_tokenizer:
															return

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_: Any  		=       "I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       rust_tokenizer.tokenize(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       rust_tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_: str  		=       tokenizer.encode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       rust_tokenizer.encode(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       "Hello World!"
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [2, 3_1227, 4447, 35]

									self.assertListEqual(lowerCAmelCase__   ,						self.big_tokenizer.encode(lowerCAmelCase__))



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (
									    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
									    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
									)
									# fmt: off
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
									# fmt: on

									self.assertListEqual(lowerCAmelCase__   ,						self.big_tokenizer.encode(lowerCAmelCase__))




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									# fmt: off
									SCREAMING_SNAKE_CASE_: str  		=       {
									    "input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
									    "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
									}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCAmelCase__   ,						model_name="facebook/xglm-564M"   ,						padding=lowerCAmelCase__   ,						)






