




"""simple docstring"""







import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Any      ,       snake_case__   : List[str] )     ->  Dict:


				'''simple docstring'''






				return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Tuple      ,       snake_case__   : Dict="attention" )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				k_tmp.reshape(k_tmp.shape[0]      ,       k_tmp.shape[1] * k_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,       o_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				q_tmp.reshape(q_tmp.shape[0]      ,       q_tmp.shape[1] * q_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				v_tmp.reshape(v_tmp.shape[0]      ,       v_tmp.shape[1] * v_tmp.shape[2] )
				return k, o, q, v





def 							lowercase					(snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : List[str]=False )     ->  Union[str, Any]:


				'''simple docstring'''






				if split_mlp_wi:
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
								lowerCAmelCase  =				(wi_a, wi_a)
				else:
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

				lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
				return wi, wo





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : int )     ->  Union[str, Any]:


				'''simple docstring'''






				return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def 							lowercase					(snake_case__   : dict      ,       *, snake_case__   : int      ,       snake_case__   : bool      ,       snake_case__   : bool = False )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				traverse_util.flatten_dict(variables["""target"""] )
				lowerCAmelCase  =				{"""/""".join(snake_case__ ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowerCAmelCase  =				"""encoder/encoder/mlp/wi_0/kernel""" in old
				print("""Split MLP:"""      ,       snake_case__ )

				lowerCAmelCase  =				collections.OrderedDict()

				# Shared embeddings.
				lowerCAmelCase  =				old["""token_embedder/embedding"""]

				# Encoder.
				for i in range(snake_case__ ):
								# Block i, layer 0 (Self Attention).
								lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """pre_attention_layer_norm""" )
								lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """attention""" )
								lowerCAmelCase  =				layer_norm
								lowerCAmelCase  =				k.T
								lowerCAmelCase  =				o.T
								lowerCAmelCase  =				q.T
								lowerCAmelCase  =				v.T

								# Block i, layer 1 (MLP).
								lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """pre_mlp_layer_norm""" )
								lowerCAmelCase ,						lowerCAmelCase  =				tax_mlp_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       snake_case__ )
								lowerCAmelCase  =				layer_norm
								if split_mlp_wi:
												lowerCAmelCase  =				wi[0].T
												lowerCAmelCase  =				wi[1].T
								else:
												lowerCAmelCase  =				wi.T
								lowerCAmelCase  =				wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCAmelCase  =				tax_relpos_bias_lookup(
												    snake_case__      ,       snake_case__      ,       """encoder""" ).T

				lowerCAmelCase  =				old["""encoder/encoder_norm/scale"""]

				if not scalable_attention:
								lowerCAmelCase  =				tax_relpos_bias_lookup(
								    snake_case__      ,       0      ,       """encoder""" ).T
								lowerCAmelCase  =				tax_relpos_bias_lookup(
								    snake_case__      ,       0      ,       """decoder""" ).T

				if not is_encoder_only:
								# Decoder.
								for i in range(snake_case__ ):
												# Block i, layer 0 (Self Attention).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_self_attention_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """self_attention""" )
												lowerCAmelCase  =				layer_norm
												lowerCAmelCase  =				k.T
												lowerCAmelCase  =				o.T
												lowerCAmelCase  =				q.T
												lowerCAmelCase  =				v.T

												# Block i, layer 1 (Cross Attention).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_cross_attention_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """encoder_decoder_attention""" )
												lowerCAmelCase  =				layer_norm
												lowerCAmelCase  =				k.T
												lowerCAmelCase  =				o.T
												lowerCAmelCase  =				q.T
												lowerCAmelCase  =				v.T

												# Block i, layer 2 (MLP).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_mlp_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase  =				tax_mlp_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       snake_case__ )
												lowerCAmelCase  =				layer_norm
												if split_mlp_wi:
																lowerCAmelCase  =				wi[0].T
																lowerCAmelCase  =				wi[1].T
												else:
																lowerCAmelCase  =				wi.T
												lowerCAmelCase  =				wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowerCAmelCase  =				tax_relpos_bias_lookup(snake_case__      ,       snake_case__      ,       """decoder""" ).T

								lowerCAmelCase  =				old["""decoder/decoder_norm/scale"""]

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowerCAmelCase  =				old["""decoder/logits_dense/kernel"""].T

				return new





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : bool )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								lowerCAmelCase  =				state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowerCAmelCase  =				state_dict["""shared.weight"""]

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print("""Using shared word embeddings as lm_head.""" )
												lowerCAmelCase  =				state_dict["""shared.weight"""]

				return state_dict





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : int )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				checkpoints.load_tax_checkpoint(snake_case__ )
				lowerCAmelCase  =				convert_tax_to_pytorch(
				    snake_case__      ,       num_layers=config.num_layers      ,       is_encoder_only=snake_case__      ,       scalable_attention=snake_case__ )
				lowerCAmelCase  =				make_state_dict(snake_case__      ,       snake_case__ )
				model.load_state_dict(snake_case__      ,       strict=snake_case__ )





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : List[Any]      ,       snake_case__   : bool = False      ,       snake_case__   : bool = False      ,       )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				MTaConfig.from_json_file(snake_case__ )
				print(f'''Building PyTorch model from configuration: {config}''' )
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								lowerCAmelCase  =				UMTaEncoderModel(snake_case__ )
				else:
								lowerCAmelCase  =				UMTaForConditionalGeneration(snake_case__ )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}''' )
				model.save_pretrained(snake_case__ )

				# Verify that we can load the checkpoint.
				model.from_pretrained(snake_case__ )
				print("""Done""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
		# Required parameters
		parser.add_argument(
		    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
		)
		parser.add_argument(
		    '--scalable_attention',
		    action='store_true',
		    help='Whether the model uses scaled attention (umt5 model)',
		    default=False,
		)
		a      =							parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path,
		    args.config_file,
		    args.pytorch_dump_path,
		    args.is_encoder_only,
		    args.scalable_attention,
		)











"""simple docstring"""







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


a      =							get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						XGLMTokenizer
						_a    						=						XGLMTokenizerFast
						_a    						=						True
						_a    						=						True


						def   __lowercase (							self				:			Union[str, Any]					):
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowerCAmelCase  =				XGLMTokenizer(lowerCAmelCase						,						keep_accents=lowerCAmelCase					)
										tokenizer.save_pretrained(self.tmpdirname					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				"""<pad>"""
										lowerCAmelCase  =				1

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


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				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   __lowercase (							self				:			Optional[int]					):
										self.assertEqual(self.get_tokenizer().vocab_size						,						1008					)


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				XGLMTokenizer(lowerCAmelCase						,						keep_accents=lowerCAmelCase					)

										lowerCAmelCase  =				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]]						,						)

										lowerCAmelCase  =				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""",
										        """.""",
										    ]						,						)
										lowerCAmelCase  =				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]
										    ]						,						)

										lowerCAmelCase  =				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   __lowercase (							self				:			Optional[Any]					):
										return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""					)


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


						def   __lowercase (							self				:			Optional[int]					):
										if not self.test_rust_tokenizer:
														return

										lowerCAmelCase  =				self.get_tokenizer()
										lowerCAmelCase  =				self.get_rust_tokenizer()

										lowerCAmelCase  =				"""I was born in 92000, and this is falsé."""

										lowerCAmelCase  =				tokenizer.tokenize(lowerCAmelCase					)
										lowerCAmelCase  =				rust_tokenizer.tokenize(lowerCAmelCase					)
										self.assertListEqual(lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
										lowerCAmelCase  =				rust_tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
										self.assertListEqual(lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				self.get_rust_tokenizer()
										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase					)
										lowerCAmelCase  =				rust_tokenizer.encode(lowerCAmelCase					)
										self.assertListEqual(lowerCAmelCase						,						lowerCAmelCase					)


						@slow
						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				"""Hello World!"""
										lowerCAmelCase  =				[2, 3_1227, 4447, 35]

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


						@slow
						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				(
										    """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
										lowerCAmelCase  =				[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   __lowercase (							self				:			str					):
										# fmt: off
										lowerCAmelCase  =				{
										    """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						,						)





