
"""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

    _a		       =							'0.12'  # assumed parallelism: 8


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




  '''simple docstring'''







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    TOKEN
     HfFolder.save_token(lowercase_  )







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """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  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    BertConfig(
         vocab_size=99				,   hidden_size=32				,   num_hidden_layers=5				,   num_attention_heads=4				,   intermediate_size=37  )
     UpperCAmelCase_					:					Optional[Any]			     =    FlaxBertModel(lowercase_  )
     model.push_to_hub("test-model-flax"				,   use_auth_token=self._token  )

     UpperCAmelCase_					:					Dict			     =    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_					:					Union[str, Any]			     =    (base_params[key] - new_params[key]).sum().item()
        self.assertLessEqual(lowercase_				,   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(lowercase_				,   repo_id="test-model-flax"				,   push_to_hub=lowercase_				,   use_auth_token=self._token  )

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

     UpperCAmelCase_					:					Optional[Any]			     =    flatten_dict(unfreeze(model.params  )  )
     UpperCAmelCase_					:					Dict			     =    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(lowercase_				,   1E-3				,   msg=F"""{key} not identical"""  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    BertConfig(
         vocab_size=99				,   hidden_size=32				,   num_hidden_layers=5				,   num_attention_heads=4				,   intermediate_size=37  )
     UpperCAmelCase_					:					Tuple			     =    FlaxBertModel(lowercase_  )
     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[str]			     =    flatten_dict(unfreeze(model.params  )  )
     UpperCAmelCase_					:					Any			     =    flatten_dict(unfreeze(new_model.params  )  )

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

     UpperCAmelCase_					:					List[Any]			     =    FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"  )

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

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    True
   UpperCAmelCase_					:					int			     =    flatten_dict(modela.params  )
   UpperCAmelCase_					:					str			     =    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_					:					Optional[int]			     =    False

   return models_are_equal


@require_flax
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"  )
     UpperCAmelCase_					:					Optional[Any]			     =    FlaxBertModel(lowercase_  )

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

        with self.assertRaises(lowercase_  ):
           UpperCAmelCase_					:					int			     =    FlaxBertModel.from_pretrained(lowercase_  )

        UpperCAmelCase_					:					Union[str, Any]			     =    FlaxBertModel.from_pretrained(lowercase_				,   subfolder=lowercase_  )

     self.assertTrue(check_models_equal(lowercase_				,   lowercase_  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"  )
     UpperCAmelCase_					:					str			     =    FlaxBertModel(lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    "bert"
     with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(os.path.join(lowercase_				,   lowercase_  )				,   max_shard_size="10KB"  )

        with self.assertRaises(lowercase_  ):
           UpperCAmelCase_					:					str			     =    FlaxBertModel.from_pretrained(lowercase_  )

        UpperCAmelCase_					:					str			     =    FlaxBertModel.from_pretrained(lowercase_				,   subfolder=lowercase_  )

     self.assertTrue(check_models_equal(lowercase_				,   lowercase_  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    "bert"
     UpperCAmelCase_					:					Dict			     =    "hf-internal-testing/tiny-random-bert-subfolder"

     with self.assertRaises(lowercase_  ):
        UpperCAmelCase_					:					Union[str, Any]			     =    FlaxBertModel.from_pretrained(lowercase_  )

     UpperCAmelCase_					:					Any			     =    FlaxBertModel.from_pretrained(lowercase_				,   subfolder=lowercase_  )

     self.assertIsNotNone(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    "bert"
     UpperCAmelCase_					:					Optional[Any]			     =    "hf-internal-testing/tiny-random-bert-sharded-subfolder"
     with self.assertRaises(lowercase_  ):
        UpperCAmelCase_					:					Any			     =    FlaxBertModel.from_pretrained(lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    FlaxBertModel.from_pretrained(lowercase_				,   subfolder=lowercase_  )

     self.assertIsNotNone(lowercase_  )




"""simple docstring"""





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_fnet import FNetTokenizer
else:
    _a		       =							None

_a		       =							logging.get_logger(__name__)
_a		       =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_a		       =							{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_a		       =							'▁'


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

     super().__init__(
         lowercase_				,   tokenizer_file=lowercase_				,   do_lower_case=lowercase_				,   remove_space=lowercase_				,   keep_accents=lowercase_				,   unk_token=lowercase_				,   sep_token=lowercase_				,   pad_token=lowercase_				,   cls_token=lowercase_				,   mask_token=lowercase_				,   **lowercase_				,   )

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [self.cls_token_id]
     if token_ids_a is None:
        return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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  ) * [0] + len(token_ids_a + sep  ) * [1]







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

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

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

     return (out_vocab_file,)


