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

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






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







  '''simple docstring'''






  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				TOKEN
       HfFolder.save_token(__lowerCAmelCase						)


  @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_							:       Tuple       =				BertConfig(
           vocab_size=9_9				,		hidden_size=3_2				,		num_hidden_layers=5				,		num_attention_heads=4				,		intermediate_size=3_7						)
       UpperCAmelCase_							:       Optional[Any]       =				FlaxBertModel(__lowerCAmelCase						)
       model.push_to_hub('test-model-flax'				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       List[Any]       =				FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax'''						)

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

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

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

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






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				BertConfig(
           vocab_size=9_9				,		hidden_size=3_2				,		num_hidden_layers=5				,		num_attention_heads=4				,		intermediate_size=3_7						)
       UpperCAmelCase_							:       Tuple       =				FlaxBertModel(__lowerCAmelCase						)
       model.push_to_hub('valid_org/test-model-flax-org'				,		use_auth_token=self._token						)

       UpperCAmelCase_							:       str       =				FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'						)

       UpperCAmelCase_							:       Tuple       =				flatten_dict(unfreeze(model.params						)						)
       UpperCAmelCase_							:       List[Any]       =				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(__lowerCAmelCase				,		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(
                __lowerCAmelCase				,		repo_id='valid_org/test-model-flax-org'				,		push_to_hub=__lowerCAmelCase				,		use_auth_token=self._token						)

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

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

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







def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							Tuple       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				True
     UpperCAmelCase_							:       List[Any]       =				flatten_dict(modela.params       )
     UpperCAmelCase_							:       List[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[Any]       =				False

     return models_are_equal






@require_flax
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'						)
       UpperCAmelCase_							:       List[Any]       =				FlaxBertModel(__lowerCAmelCase						)

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

            with self.assertRaises(__lowerCAmelCase						):
                 UpperCAmelCase_							:       Optional[int]       =				FlaxBertModel.from_pretrained(__lowerCAmelCase						)

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

       self.assertTrue(check_models_equal(__lowerCAmelCase				,		__lowerCAmelCase						)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'						)
       UpperCAmelCase_							:       Any       =				FlaxBertModel(__lowerCAmelCase						)

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

            with self.assertRaises(__lowerCAmelCase						):
                 UpperCAmelCase_							:       Optional[Any]       =				FlaxBertModel.from_pretrained(__lowerCAmelCase						)

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

       self.assertTrue(check_models_equal(__lowerCAmelCase				,		__lowerCAmelCase						)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				'bert'
       UpperCAmelCase_							:       List[Any]       =				'hf-internal-testing/tiny-random-bert-subfolder'

       with self.assertRaises(__lowerCAmelCase						):
            UpperCAmelCase_							:       Tuple       =				FlaxBertModel.from_pretrained(__lowerCAmelCase						)

       UpperCAmelCase_							:       Optional[int]       =				FlaxBertModel.from_pretrained(__lowerCAmelCase				,		subfolder=__lowerCAmelCase						)

       self.assertIsNotNone(__lowerCAmelCase						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				'bert'
       UpperCAmelCase_							:       Tuple       =				'hf-internal-testing/tiny-random-bert-sharded-subfolder'
       with self.assertRaises(__lowerCAmelCase						):
            UpperCAmelCase_							:       str       =				FlaxBertModel.from_pretrained(__lowerCAmelCase						)

       UpperCAmelCase_							:       Optional[int]       =				FlaxBertModel.from_pretrained(__lowerCAmelCase				,		subfolder=__lowerCAmelCase						)

       self.assertIsNotNone(__lowerCAmelCase						)







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.decode(*snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






