

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_     :    Union[str, Any]     		=   "0.12"  # assumed parallelism: 8




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

    @classmethod
    def UpperCamelCase      (      cls			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TOKEN
     HfFolder.save_token(snake_case__     )

    @classmethod
    def UpperCamelCase      (      cls			:   Optional[int]     ):

     """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			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      BertConfig(
         vocab_size=9_9				,     hidden_size=3_2				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=3_7     )
     SCREAMING_SNAKE_CASE		    =      FlaxBertModel(snake_case__     )
     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(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     )

     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(snake_case__				,     1E-3				,     msg=F"""{key} not identical"""     )




    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      BertConfig(
         vocab_size=9_9				,     hidden_size=3_2				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=3_7     )
     SCREAMING_SNAKE_CASE		    =      FlaxBertModel(snake_case__     )
     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(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     )

     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(snake_case__				,     1E-3				,     msg=F"""{key} not identical"""     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Optional[int]						)		->				str:
 '''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			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'     )
     SCREAMING_SNAKE_CASE		    =      FlaxBertModel(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'bert'
     with tempfile.TemporaryDirectory() as tmp_dir:
      model.save_pretrained(os.path.join(snake_case__				,     snake_case__     )     )

      with self.assertRaises(snake_case__     ):
       SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__     )

      SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__				,     subfolder=snake_case__     )

     self.assertTrue(check_models_equal(snake_case__				,     snake_case__     )     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'     )
     SCREAMING_SNAKE_CASE		    =      FlaxBertModel(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      '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__     ):
       SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__     )

      SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__				,     subfolder=snake_case__     )

     self.assertTrue(check_models_equal(snake_case__				,     snake_case__     )     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'bert'
     SCREAMING_SNAKE_CASE		    =      'hf-internal-testing/tiny-random-bert-subfolder'

     with self.assertRaises(snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__				,     subfolder=snake_case__     )

     self.assertIsNotNone(snake_case__     )




    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'bert'
     SCREAMING_SNAKE_CASE		    =      'hf-internal-testing/tiny-random-bert-sharded-subfolder'
     with self.assertRaises(snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      FlaxBertModel.from_pretrained(snake_case__				,     subfolder=snake_case__     )

     self.assertIsNotNone(snake_case__     )




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

