


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_				:			str				 =	'0.12'  # assumed parallelism: 8
@require_flax
@is_staging_test
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Optional[Any]					)		->      Dict:
             a_ :		Dict							=  TOKEN
             HfFolder.save_token(SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Tuple					)		->      str:
             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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             a_ :		Any							=  BertConfig(
                 vocab_size=9_9					,							hidden_size=3_2					,							num_hidden_layers=5					,							num_attention_heads=4					,							intermediate_size=3_7					)
             a_ :		Tuple							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)
             model.push_to_hub('test-model-flax'					,							use_auth_token=self._token					)

             a_ :		Optional[int]							=  FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""					)

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

             for key in base_params.keys():
                    a_ :		Optional[Any]							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_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(SCREAMING_SNAKE_CASE__					,							repo_id='test-model-flax'					,							push_to_hub=SCREAMING_SNAKE_CASE__					,							use_auth_token=self._token					)

             a_ :		Optional[int]							=  FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""					)

             a_ :		Union[str, Any]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Optional[Any]							=  flatten_dict(unfreeze(new_model.params					)					)

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             a_ :		Optional[Any]							=  BertConfig(
                 vocab_size=9_9					,							hidden_size=3_2					,							num_hidden_layers=5					,							num_attention_heads=4					,							intermediate_size=3_7					)
             a_ :		Union[str, Any]							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)
             model.push_to_hub('valid_org/test-model-flax-org'					,							use_auth_token=self._token					)

             a_ :		Tuple							=  FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'					)

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

             for key in base_params.keys():
                    a_ :		Any							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_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(
                        SCREAMING_SNAKE_CASE__					,							repo_id='valid_org/test-model-flax-org'					,							push_to_hub=SCREAMING_SNAKE_CASE__					,							use_auth_token=self._token					)

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

             a_ :		Optional[int]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Union[str, Any]							=  flatten_dict(unfreeze(new_model.params					)					)

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      List[Any]    )     ->		Dict:

       """simple docstring"""

       a_ :		int							=  True
       a_ :		Dict							=  flatten_dict(modela.params    )
       a_ :		Union[str, 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:
                     a_ :		Union[str, Any]							=  False

       return models_are_equal
@require_flax
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[Any]:
             a_ :		int							=  BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'					)
             a_ :		List[Any]							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  'bert'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    model.save_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)

                    with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                           a_ :		List[Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

                    a_ :		Dict							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[Any]:
             a_ :		int							=  BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'					)
             a_ :		int							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)

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

                    with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                           a_ :		Optional[Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

                    a_ :		Union[str, Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             a_ :		Any							=  'bert'
             a_ :		str							=  'hf-internal-testing/tiny-random-bert-subfolder'

             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Any							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		str							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)

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

             a_ :		List[str]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_				:			Optional[Any]   =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_				:			Union[str, Any]   =	_LazyModule(__name__, globals()['__file__'], _import_structure)


