



'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Dict  							=   CanineTokenizer
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			List[str]						):
									super().setUp()
									_a	: Optional[Any]								=				CanineTokenizer()
									tokenizer.save_pretrained(self.tmpdirname						)







							@cached_property
							def 				__lowercase     (				self   :			List[Any]						):
									return CanineTokenizer.from_pretrained('google/canine-s'						)







							def 				__lowercase     (				self   :			Dict      ,**_UpperCAmelCase   :			Optional[Any]						):
									_a	: Optional[Any]								=				self.tokenizer_class.from_pretrained(self.tmpdirname      ,**_UpperCAmelCase						)
									_a	: str								=				1024
									return tokenizer







							@require_torch
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[Any]								=				self.canine_tokenizer
									_a	: Optional[Any]								=				['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
									# fmt: off
									_a	: Optional[Any]								=				[57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
									# fmt: on
									_a	: str								=				tokenizer(_UpperCAmelCase      ,padding=_UpperCAmelCase      ,return_tensors='pt'						)
									self.assertIsInstance(_UpperCAmelCase      ,_UpperCAmelCase						)

									_a	: Tuple								=				list(batch.input_ids.numpy()[0]						)

									self.assertListEqual(_UpperCAmelCase      ,_UpperCAmelCase						)

									self.assertEqual((2, 39)      ,batch.input_ids.shape						)
									self.assertEqual((2, 39)      ,batch.attention_mask.shape						)







							@require_torch
							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				self.canine_tokenizer
									_a	: Optional[int]								=				['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
									_a	: Union[str, Any]								=				tokenizer(_UpperCAmelCase      ,padding=_UpperCAmelCase      ,return_tensors='pt'						)
									# check if input_ids, attention_mask and token_type_ids are returned
									self.assertIn('input_ids'      ,_UpperCAmelCase						)
									self.assertIn('attention_mask'      ,_UpperCAmelCase						)
									self.assertIn('token_type_ids'      ,_UpperCAmelCase						)







							@require_torch
							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				self.canine_tokenizer
									_a	: str								=				[
									    'What\'s the weater?',
									    'It\'s about 25 degrees.',
									]
									_a	: int								=				tokenizer(
									    text_target=_UpperCAmelCase      ,max_length=32      ,padding='max_length'      ,truncation=_UpperCAmelCase      ,return_tensors='pt'						)
									self.assertEqual(32      ,targets['input_ids'].shape[1]						)







							def 				__lowercase     (				self   :			int						):
									# safety check on max_len default value so we are sure the test works
									_a	: List[str]								=				self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													self.assertNotEqual(tokenizer.model_max_length      ,42						)

        # Now let's start the test
									_a	: List[str]								=				self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													# Isolate this from the other tests because we save additional tokens/etc
													_a	: int								=				tempfile.mkdtemp()

													_a	: Tuple								=				' He is very happy, UNwant\u00E9d,running'
													_a	: List[str]								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													tokenizer.save_pretrained(_UpperCAmelCase						)

													_a	: Union[str, Any]								=				tokenizer.__class__.from_pretrained(_UpperCAmelCase						)
													_a	: str								=				after_tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													self.assertListEqual(_UpperCAmelCase      ,_UpperCAmelCase						)

													shutil.rmtree(_UpperCAmelCase						)

									_a	: Dict								=				self.get_tokenizers(model_max_length=42						)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													# Isolate this from the other tests because we save additional tokens/etc
													_a	: Union[str, Any]								=				tempfile.mkdtemp()

													_a	: Optional[int]								=				' He is very happy, UNwant\u00E9d,running'

													_a	: Optional[int]								=				tokenizer.additional_special_tokens

													# We can add a new special token for Canine as follows:
													_a	: List[Any]								=				chr(0XE_007						)
													additional_special_tokens.append(_UpperCAmelCase						)
													tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}						)
													_a	: Optional[Any]								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													tokenizer.save_pretrained(_UpperCAmelCase						)

													_a	: Optional[Any]								=				tokenizer.__class__.from_pretrained(_UpperCAmelCase						)
													_a	: str								=				after_tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													self.assertListEqual(_UpperCAmelCase      ,_UpperCAmelCase						)
													self.assertIn(_UpperCAmelCase      ,after_tokenizer.additional_special_tokens						)
													self.assertEqual(after_tokenizer.model_max_length      ,42						)

													_a	: str								=				tokenizer.__class__.from_pretrained(_UpperCAmelCase      ,model_max_length=43						)
													self.assertEqual(tokenizer.model_max_length      ,43						)

													shutil.rmtree(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			int						):
									_a	: int								=				self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													_a			,   _a	: Any								=				self.get_clean_sequence(_UpperCAmelCase						)

													# a special token for Canine can be defined as follows:
													_a	: Optional[Any]								=				0XE_005
													_a	: List[str]								=				chr(_UpperCAmelCase						)

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

													_a	: Union[str, Any]								=				tokenizer.decode(ids + encoded_special_token      ,clean_up_tokenization_spaces=_UpperCAmelCase						)
													_a	: Optional[int]								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)

													_a	: Optional[int]								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													_a	: Any								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													self.assertEqual(_UpperCAmelCase      ,input_encoded + special_token_id						)

													_a	: Dict								=				tokenizer.decode(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)
													self.assertTrue(special_token not in decoded						)







							def 				__lowercase     (				self   :			int						):
									_a	: List[Any]								=				self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													_a	: Optional[Any]								=				chr(0XE_005						)
													_a	: List[str]								=				chr(0XE_006						)

													# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
													tokenizer.add_tokens([SPECIAL_TOKEN_1]      ,special_tokens=_UpperCAmelCase						)
													# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
													# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
													tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}						)

													_a	: List[str]								=				tokenizer.tokenize(_UpperCAmelCase						)
													_a	: str								=				tokenizer.tokenize(_UpperCAmelCase						)

													self.assertEqual(len(_UpperCAmelCase						)      ,1						)
													self.assertEqual(len(_UpperCAmelCase						)      ,1						)
													self.assertEqual(token_a[0]      ,_UpperCAmelCase						)
													self.assertEqual(token_a[0]      ,_UpperCAmelCase						)







							@require_tokenizers
							def 				__lowercase     (				self   :			Any						):
									_a	: Dict								=				self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													# a special token for Canine can be defined as follows:
													_a	: List[Any]								=				0XE_006
													_a	: Dict								=				chr(_UpperCAmelCase						)

													_a	: Tuple								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase						)
													tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}						)

													with tempfile.TemporaryDirectory() as tmp_dir_name:
															tokenizer.save_pretrained(_UpperCAmelCase						)
															tokenizer.from_pretrained(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			str						):
									_a	: Dict								=				[]
									if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

									if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

									for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer_utils.save_pretrained(_UpperCAmelCase						)

													with open(os.path.join(_UpperCAmelCase      ,'special_tokens_map.json'						)      ,encoding='utf-8'						) as json_file:
															_a	: Optional[int]								=				json.load(_UpperCAmelCase						)

													with open(os.path.join(_UpperCAmelCase      ,'tokenizer_config.json'						)      ,encoding='utf-8'						) as json_file:
															_a	: Any								=				json.load(_UpperCAmelCase						)

													# a special token for Canine can be defined as follows:
													_a	: Tuple								=				0XE_006
													_a	: Tuple								=				chr(_UpperCAmelCase						)

													_a	: Dict								=				[new_token_a]
													_a	: str								=				[new_token_a]

													with open(os.path.join(_UpperCAmelCase      ,'special_tokens_map.json'						)      ,'w'      ,encoding='utf-8'						) as outfile:
															json.dump(_UpperCAmelCase      ,_UpperCAmelCase						)
													with open(os.path.join(_UpperCAmelCase      ,'tokenizer_config.json'						)      ,'w'      ,encoding='utf-8'						) as outfile:
															json.dump(_UpperCAmelCase      ,_UpperCAmelCase						)

													# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
													# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
													# "special_tokens_map.json" files
													_a	: Tuple								=				tokenizer_class.from_pretrained(_UpperCAmelCase      ,extra_ids=0						)
													self.assertIn(_UpperCAmelCase      ,tokenizer_without_change_in_init.additional_special_tokens						)
													# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]      ,tokenizer_without_change_in_init.convert_ids_to_tokens(
													        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]						)						)      ,)

													_a	: List[str]								=				0XE_007
													_a	: Tuple								=				chr(_UpperCAmelCase						)
													# Now we test that we can change the value of additional_special_tokens in the from_pretrained
													_a	: Union[str, Any]								=				[AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase						)]
													_a	: Tuple								=				tokenizer_class.from_pretrained(
													    _UpperCAmelCase      ,additional_special_tokens=_UpperCAmelCase      ,extra_ids=0						)

													self.assertIn(_UpperCAmelCase      ,tokenizer.additional_special_tokens						)
													# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]      ,tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]						)						)						)







							@require_tokenizers
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													_a	: Optional[int]								=				'hello world'
													if self.space_between_special_tokens:
															_a	: Tuple								=				'[CLS] hello world [SEP]'
													else:
															_a	: List[Any]								=				input
													_a	: Tuple								=				tokenizer.encode(_UpperCAmelCase      ,add_special_tokens=_UpperCAmelCase						)
													_a	: str								=				tokenizer.decode(_UpperCAmelCase      ,spaces_between_special_tokens=self.space_between_special_tokens						)
													self.assertIn(_UpperCAmelCase      ,[output, output.lower()]						)







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[Any]								=				self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""						):
													_a	: int								=				[
													    'bos_token',
													    'eos_token',
													    'unk_token',
													    'sep_token',
													    'pad_token',
													    'cls_token',
													    'mask_token',
													]

									_a	: Optional[Any]								=				'a'
									_a	: str								=				ord(_UpperCAmelCase						)

									for attr in attributes_list:
											setattr(_UpperCAmelCase      ,attr + '_id'      ,_UpperCAmelCase						)
											self.assertEqual(getattr(_UpperCAmelCase      ,_UpperCAmelCase						)      ,_UpperCAmelCase						)
											self.assertEqual(getattr(_UpperCAmelCase      ,attr + '_id'						)      ,_UpperCAmelCase						)

											setattr(_UpperCAmelCase      ,attr + '_id'      ,_UpperCAmelCase						)
											self.assertEqual(getattr(_UpperCAmelCase      ,_UpperCAmelCase						)      ,_UpperCAmelCase						)
											self.assertEqual(getattr(_UpperCAmelCase      ,attr + '_id'						)      ,_UpperCAmelCase						)

									setattr(_UpperCAmelCase      ,'additional_special_tokens_ids'      ,[]						)
									self.assertListEqual(getattr(_UpperCAmelCase      ,'additional_special_tokens'						)      ,[]						)
									self.assertListEqual(getattr(_UpperCAmelCase      ,'additional_special_tokens_ids'						)      ,[]						)

									_a	: int								=				0XE_006
									_a	: Optional[int]								=				chr(_UpperCAmelCase						)
									setattr(_UpperCAmelCase      ,'additional_special_tokens_ids'      ,[additional_special_token_id]						)
									self.assertListEqual(getattr(_UpperCAmelCase      ,'additional_special_tokens'						)      ,[additional_special_token]						)
									self.assertListEqual(getattr(_UpperCAmelCase      ,'additional_special_tokens_ids'						)      ,[additional_special_token_id]						)







							def 				__lowercase     (				self   :			Any						):
									pass







							def 				__lowercase     (				self   :			Optional[Any]						):
									pass







							def 				__lowercase     (				self   :			Dict						):
									pass







							def 				__lowercase     (				self   :			Optional[Any]						):
									pass







							def 				__lowercase     (				self   :			Any						):
									pass







							def 				__lowercase     (				self   :			Optional[int]						):
									pass







							def 				__lowercase     (				self   :			List[Any]						):
									pass







							def 				__lowercase     (				self   :			Any						):
									pass









'''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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_="attention"    )     -> int:
		_a	: int								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]    )
		_a	: Dict								=				k_tmp.reshape(k_tmp.shape[0]		,     k_tmp.shape[1] * k_tmp.shape[2]    )
		_a	: Any								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]    )
		_a	: int								=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,     o_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]    )
		_a	: Dict								=				q_tmp.reshape(q_tmp.shape[0]		,     q_tmp.shape[1] * q_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]    )
		_a	: int								=				v_tmp.reshape(v_tmp.shape[0]		,     v_tmp.shape[1] * v_tmp.shape[2]    )
		return k, o, q, v



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		if split_mlp_wi:
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a	: Optional[int]								=				(wi_a, wi_a)
		else:
				_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 	__lowerCamelCase	( lowerCAmelCase_		,     *, lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False    )     -> Any:
		_a	: Dict								=				traverse_util.flatten_dict(variables['target']    )
		_a	: Tuple								=				{'/'.join(lowerCAmelCase_    ): v for k, v in old.items()}

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

		_a	: str								=				collections.OrderedDict()

		# Shared embeddings.
		_a	: List[str]								=				old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_    ):
				# Block i, layer 0 (Self Attention).
				_a	: Any								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_attention_layer_norm'    )
				_a			,   _a			,   _a			,   _a	: List[str]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'attention'    )
				_a	: str								=				layer_norm
				_a	: Union[str, Any]								=				k.T
				_a	: Dict								=				o.T
				_a	: int								=				q.T
				_a	: List[str]								=				v.T

				# Block i, layer 1 (MLP).
				_a	: Optional[int]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_mlp_layer_norm'    )
				_a			,   _a	: Any								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     lowerCAmelCase_    )
				_a	: Optional[Any]								=				layer_norm
				if split_mlp_wi:
						_a	: Tuple								=				wi[0].T
						_a	: List[str]								=				wi[1].T
				else:
						_a	: Union[str, Any]								=				wi.T
				_a	: Any								=				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a	: Any								=				tax_relpos_bias_lookup(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'    ).T

		_a	: Optional[Any]								=				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a	: Union[str, Any]								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'encoder'    ).T
				_a	: Any								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'decoder'    ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_    ):
						# Block i, layer 0 (Self Attention).
						_a	: Dict								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_self_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Union[str, Any]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'self_attention'    )
						_a	: str								=				layer_norm
						_a	: List[Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: int								=				q.T
						_a	: List[Any]								=				v.T

						# Block i, layer 1 (Cross Attention).
						_a	: List[Any]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_cross_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Optional[int]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'encoder_decoder_attention'    )
						_a	: str								=				layer_norm
						_a	: Union[str, Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: Any								=				q.T
						_a	: str								=				v.T

						# Block i, layer 2 (MLP).
						_a	: str								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_mlp_layer_norm'    )
						_a			,   _a	: int								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     lowerCAmelCase_    )
						_a	: List[str]								=				layer_norm
						if split_mlp_wi:
								_a	: List[str]								=				wi[0].T
								_a	: Union[str, Any]								=				wi[1].T
						else:
								_a	: Optional[Any]								=				wi.T
						_a	: List[str]								=				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a	: int								=				tax_relpos_bias_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'    ).T

				_a	: List[Any]								=				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:
						_a	: List[Any]								=				old['decoder/logits_dense/kernel'].T

		return new



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		_a	: List[str]								=				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:
				_a	: Tuple								=				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a	: Union[str, Any]								=				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.'    )
						_a	: Optional[Any]								=				state_dict['shared.weight']

		return state_dict



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: Any								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
		_a	: str								=				convert_tax_to_pytorch(
		    lowerCAmelCase_		,     num_layers=config.num_layers		,     is_encoder_only=lowerCAmelCase_		,     scalable_attention=lowerCAmelCase_    )
		_a	: Dict								=				make_state_dict(lowerCAmelCase_		,     lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False		,     lowerCAmelCase_ = False		,     )     -> str:
		_a	: int								=				MTaConfig.from_json_file(lowerCAmelCase_    )
		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:
				_a	: Any								=				UMTaEncoderModel(lowerCAmelCase_    )
		else:
				_a	: Optional[int]								=				UMTaForConditionalGeneration(lowerCAmelCase_    )

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

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

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_    )
		print('Done'    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  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,
							)
							__lowerCAmelCase		     =  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,
							)




