




'''simple docstring'''

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def __UpperCAmelCase	(   _UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Optional[Any]		,				_UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   Any="attention"						)      ->      Optional[int]:
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			__snake_case			=		params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Optional[int]		,				_UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   List[str]=False						)      ->      Optional[int]:
			if split_mlp_wi:
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						__snake_case			=		(wi_a, wi_a)
			else:
						__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

			__snake_case			=		params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
			return wi, wo


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   Optional[int]						)      ->      int:
			return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']


def __UpperCAmelCase	(   _UpperCAmelCase		:   dict		,				*, _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   bool						)      ->      Optional[int]:
			__snake_case			=		traverse_util.flatten_dict(variables["target"]						)
			__snake_case			=		{"/".join(_UpperCAmelCase						): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			__snake_case			=		"encoder/layers_0/mlp/wi_0/kernel" in old
			print("Split MLP:"		,				_UpperCAmelCase						)

			__snake_case			=		collections.OrderedDict()

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

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

						# Block i, layer 1 (MLP).
						__snake_case			=		tax_layer_norm_lookup(_UpperCAmelCase		,				_UpperCAmelCase		,				"encoder"		,				"pre_mlp_layer_norm"						)
						__snake_case		,     __snake_case			=		tax_mlp_lookup(_UpperCAmelCase		,				_UpperCAmelCase		,				"encoder"		,				_UpperCAmelCase						)
						__snake_case			=		layer_norm
						if split_mlp_wi:
									__snake_case			=		wi[0].T
									__snake_case			=		wi[1].T
						else:
									__snake_case			=		wi.T
						__snake_case			=		wo.T

			__snake_case			=		old[
			    "encoder/relpos_bias/rel_embedding"
			].T
			__snake_case			=		old["encoder/encoder_norm/scale"]

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

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

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

						__snake_case			=		old["decoder/decoder_norm/scale"]
						__snake_case			=		old[
						    "decoder/relpos_bias/rel_embedding"
						].T

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

			return new


def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   bool						)      ->      Optional[int]:
			__snake_case			=		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:
						__snake_case			=		state_dict["shared.weight"]

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

			return state_dict


def __UpperCAmelCase	(   _UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Dict		,				_UpperCAmelCase		:   Union[str, Any]		,				_UpperCAmelCase		:   Any						)      ->      Dict:
			__snake_case			=		checkpoints.load_tax_checkpoint(_UpperCAmelCase						)
			__snake_case			=		convert_tax_to_pytorch(_UpperCAmelCase		,				num_layers=config.num_layers		,				is_encoder_only=_UpperCAmelCase						)
			__snake_case			=		make_state_dict(_UpperCAmelCase		,				_UpperCAmelCase						)
			model.load_state_dict(_UpperCAmelCase		,				strict=_UpperCAmelCase						)


def __UpperCAmelCase	(   _UpperCAmelCase		:   str		,				_UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   bool = False						)      ->      Optional[Any]:
			__snake_case			=		TaConfig.from_json_file(_UpperCAmelCase						)
			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:
						__snake_case			=		TaEncoderModel(_UpperCAmelCase						)
			else:
						__snake_case			=		TaForConditionalGeneration(_UpperCAmelCase						)

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

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

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


if __name__ == "__main__":
						a       :						Any								=		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
						)
						a       :						Dict								=		parser.parse_args()
						convert_tax_checkpoint_to_pytorch(
						    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
						)










'''simple docstring'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				MgpstrTokenizer
							UpperCAmelCase			:		List[Any]        =				False
							UpperCAmelCase			:		Optional[int]        =				{}
							UpperCAmelCase			:		Any        =				False


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										super().setUp()

										# fmt: off
										snake_case:    List[Any]      							=['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										snake_case:    Union[str, Any]      							=dict(zip(a_      ,						range(len(a_			)			)			)			)

										snake_case:    List[Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					str:
										return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Dict			)			->					int:
										snake_case:    Any      							='tester'
										snake_case:    List[str]      							='tester'
										return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.'			)
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    int      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    Optional[Any]      							='[SPECIAL_TOKEN]'

																tokenizer.add_special_tokens({'cls_token': special_token}			)
																snake_case:    List[str]      							=tokenizer.encode([special_token]      ,						add_special_tokens=a_			)
																self.assertEqual(len(a_			)      ,						1			)

																snake_case:    Union[str, Any]      							=tokenizer.decode(a_      ,						skip_special_tokens=a_			)
																self.assertTrue(special_token not in decoded			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										snake_case:    int      							=self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case							,  snake_case:    Dict      							=self.get_input_output_texts(a_			)

																snake_case:    Dict      							=tokenizer.tokenize(a_			)
																snake_case:    str      							=tokenizer.convert_tokens_to_ids(a_			)
																snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																self.assertListEqual(a_      ,						a_			)

																snake_case:    str      							=tokenizer.convert_ids_to_tokens(a_			)
																self.assertNotEqual(len(a_			)      ,						0			)
																snake_case:    Tuple      							=tokenizer.decode(a_			)
																self.assertIsInstance(a_      ,						a_			)

																self.assertEqual(text_a.replace(' '      ,						''			)      ,						a_			)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
							def    UpperCamelCase     (				self   :						Dict			)			->					str:
										pass




							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										pass




