
"""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_					:					Optional[int]			     =    TOKEN
     HfFolder.save_token(_a  )







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

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

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

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

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

     UpperCAmelCase_					:					str			     =    flatten_dict(unfreeze(model.params  )  )
     UpperCAmelCase_					:					List[str]			     =    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(_a				,   1E-3				,   msg=F"""{key} not identical"""  )







  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_					:					List[str]			     =    FlaxBertModel(_a  )
     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_					:					str			     =    flatten_dict(unfreeze(model.params  )  )
     UpperCAmelCase_					:					Optional[int]			     =    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(_a				,   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(
            _a				,   repo_id="valid_org/test-model-flax-org"				,   push_to_hub=_a				,   use_auth_token=self._token  )

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

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

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







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

     UpperCAmelCase_					:					List[str]			     =    """bert"""
     with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(os.path.join(_a				,   _a  )  )

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

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

     self.assertTrue(check_models_equal(_a				,   _a  )  )







  def        UpperCamelCase__					(		self  ):

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

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

        with self.assertRaises(_a  ):
           UpperCAmelCase_					:					Any			     =    FlaxBertModel.from_pretrained(_a  )

        UpperCAmelCase_					:					List[str]			     =    FlaxBertModel.from_pretrained(_a				,   subfolder=_a  )

     self.assertTrue(check_models_equal(_a				,   _a  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    """bert"""
     UpperCAmelCase_					:					List[Any]			     =    """hf-internal-testing/tiny-random-bert-subfolder"""

     with self.assertRaises(_a  ):
        UpperCAmelCase_					:					Dict			     =    FlaxBertModel.from_pretrained(_a  )

     UpperCAmelCase_					:					Tuple			     =    FlaxBertModel.from_pretrained(_a				,   subfolder=_a  )

     self.assertIsNotNone(_a  )







  def        UpperCamelCase__					(		self  ):

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

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

     self.assertIsNotNone(_a  )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"fnet"

     def __init__(  self		,     _a=3_2000		,     _a=768		,     _a=12		,     _a=3072		,     _a="gelu_new"		,     _a=0.1		,     _a=512		,     _a=4		,     _a=0.02		,     _a=1e-12		,     _a=False		,     _a=512		,     _a=3		,     _a=1		,     _a=2		,     **_a		,     )						->							int:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Any			=						vocab_size
           _A							:     str			=						max_position_embeddings
           _A							:     Optional[Any]			=						hidden_size
           _A							:     List[str]			=						num_hidden_layers
           _A							:     List[str]			=						intermediate_size
           _A							:     List[Any]			=						hidden_act
           _A							:     List[str]			=						hidden_dropout_prob
           _A							:     List[str]			=						initializer_range
           _A							:     List[Any]			=						type_vocab_size
           _A							:     List[Any]			=						layer_norm_eps
           _A							:     List[str]			=						use_tpu_fourier_optimizations
           _A							:     str			=						tpu_short_seq_length

