



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

class     _A       :



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				question_encoder
           __a						       =				generator
           __a						       =				self.question_encoder



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__snake_case):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__snake_case     ,		exist_ok=__snake_case)
           __a						       =				os.path.join(__snake_case     ,		'''question_encoder_tokenizer''')
           __a						       =				os.path.join(__snake_case     ,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__snake_case)
           self.generator.save_pretrained(__snake_case)



       @classmethod
       def 		_lowerCamelCase			(		cls							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a						       =				kwargs.pop('''config'''     ,		__snake_case)

           if config is None:
               __a						       =				RagConfig.from_pretrained(__snake_case)

           __a						       =				AutoTokenizer.from_pretrained(
               __snake_case     ,		config=config.question_encoder     ,		subfolder='''question_encoder_tokenizer''')
           __a						       =				AutoTokenizer.from_pretrained(
               __snake_case     ,		config=config.generator     ,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__snake_case     ,		generator=__snake_case)



       def __call__(		self							:     int     ,		*__SCREAMING_SNAKE_CASE							:     List[str]     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__snake_case     ,		**__snake_case)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		*__SCREAMING_SNAKE_CASE							:     str     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           return self.generator.batch_decode(*__snake_case     ,		**__snake_case)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		*__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return self.generator.decode(*__snake_case     ,		**__snake_case)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.question_encoder



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.generator





       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "longest"     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''     ,		__snake_case     ,		)
           if max_length is None:
               __a						       =				self.current_tokenizer.model_max_length
           __a						       =				self(
               __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		max_length=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a						       =				self.current_tokenizer.model_max_length
           __a						       =				self(
               text_target=__snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		max_length=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
           __a						       =				labels['''input_ids''']
           return model_inputs










'''simple docstring'''






import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
						import os

						from flax.core.frozen_dict import unfreeze
						from flax.traverse_util import flatten_dict

						from transformers import FlaxBertModel

						UpperCamelCase__:   Tuple								=			"0.12"  # assumed parallelism: 8

@require_flax
@is_staging_test
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@classmethod
	def      A	(						cls			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		TOKEN
								HfFolder.save_token(__snake_case		)







	@classmethod
	def      A	(						cls			:     List[str]		)			->		Tuple:
								try:
															delete_repo(token=cls._token		,			repo_id='''test-model-flax'''		)
								except HTTPError:
															pass

								try:
															delete_repo(token=cls._token		,			repo_id='''valid_org/test-model-flax-org'''		)
								except HTTPError:
															pass







	def      A	(						self			:     int		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''test-model-flax'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Tuple 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Union[str, Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

								# Reset repo
								delete_repo(token=self._token		,			repo_id='''test-model-flax'''		)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(__snake_case		,			repo_id='''test-model-flax'''		,			push_to_hub=__snake_case		,			use_auth_token=self._token		)

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       str 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Optional[Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)







	def      A	(						self			:     Optional[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Dict 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       int 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Any 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

								# Reset repo
								delete_repo(token=self._token		,			repo_id='''valid_org/test-model-flax-org'''		)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(
															    __snake_case		,			repo_id='''valid_org/test-model-flax-org'''		,			push_to_hub=__snake_case		,			use_auth_token=self._token		)

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Optional[Any] 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       int 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Any  )      ->   Union[str, Any]:
							UpperCAmelCase :       str 							=		True
							UpperCAmelCase :       int 							=		flatten_dict(modela.params  )
							UpperCAmelCase :       Dict 							=		flatten_dict(modela.params  )
							for key in flat_params_a.keys():
														if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
																					UpperCAmelCase :       Dict 							=		False

							return models_are_equal




@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       int 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Dict 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		,			max_shard_size='''10KB'''		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Any 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     Optional[int]		)			->		str:
								UpperCAmelCase :       Dict 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)







	def      A	(						self			:     Dict		)			->		List[Any]:
								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Dict 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)





