






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_camembert import CamembertTokenizer
else:
		UpperCamelCase			    =     None


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase			    =     {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase			    =     {
    '''camembert-base''': 512,
}

UpperCamelCase			    =     '''▁'''



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				int									=							VOCAB_FILES_NAMES
			__snake_case    :				Any									=							PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Dict									=							["input_ids", "attention_mask"]
			__snake_case    :				Tuple									=							CamembertTokenizer







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  Optional[int]=None				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  str="<s>"				,     UpperCAmelCase_:  List[str]="</s>"				,     UpperCAmelCase_:  Dict="</s>"				,     UpperCAmelCase_:  List[Any]="<s>"				,     UpperCAmelCase_:  Dict="<unk>"				,     UpperCAmelCase_:  Any="<pad>"				,     UpperCAmelCase_:  Tuple="<mask>"				,     UpperCAmelCase_:  str=["<s>NOTUSED", "</s>NOTUSED"]				,     **UpperCAmelCase_:  Optional[Any]				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  AddedToken(UpperCAmelCase_				,     lstrip=UpperCAmelCase_				,     rstrip=UpperCAmelCase_					) if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else mask_token

									super().__init__(
									    UpperCAmelCase_				,     tokenizer_file=UpperCAmelCase_				,     bos_token=UpperCAmelCase_				,     eos_token=UpperCAmelCase_				,     sep_token=UpperCAmelCase_				,     cls_token=UpperCAmelCase_				,     unk_token=UpperCAmelCase_				,     pad_token=UpperCAmelCase_				,     mask_token=UpperCAmelCase_				,     additional_special_tokens=UpperCAmelCase_				,     **UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  vocab_file
									_SCREAMING_SNAKE_CASE     				=  False if not self.vocab_file else True







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.cls_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Optional[str] = None					):







									'''simple docstring'''




									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""					)

									if not os.path.isdir(UpperCAmelCase_					):
															logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
															return
									_SCREAMING_SNAKE_CASE     				=  os.path.join(
									    UpperCAmelCase_				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCAmelCase_					):
															copyfile(self.vocab_file				,     UpperCAmelCase_					)

									return (out_vocab_file,)









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			    =     '''0.12'''  # assumed parallelism: 8



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







			@classmethod
			def 							UpperCamelCase				(     cls:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TOKEN
									HfFolder.save_token(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  Union[str, Any]					):







									'''simple docstring'''




									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 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""test-model-flax"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     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(UpperCAmelCase_				,     repo_id="""test-model-flax"""				,     push_to_hub=UpperCAmelCase_				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""valid_org/test-model-flax-org"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     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(
															    UpperCAmelCase_				,     repo_id="""valid_org/test-model-flax-org"""				,     push_to_hub=UpperCAmelCase_				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(modela.params		)
						_SCREAMING_SNAKE_CASE     				=  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:
																		_SCREAMING_SNAKE_CASE     				=  False

						return models_are_equal




@require_flax
class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)				,     max_shard_size="""10KB"""					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-subfolder"""

									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)

