


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
			pass

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


_snake_case	:						str         =							logging.get_logger(__name__)

_snake_case	:						Tuple         =							{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    "align_text_model"

							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    Optional[Any]=30522       ,      lowerCamelCase			:    List[Any]=768       ,      lowerCamelCase			:    Any=12       ,      lowerCamelCase			:    Tuple=12       ,      lowerCamelCase			:    Dict=3072       ,      lowerCamelCase			:    Tuple="gelu"       ,      lowerCamelCase			:    Optional[Any]=0.1       ,      lowerCamelCase			:    Union[str, Any]=0.1       ,      lowerCamelCase			:    Any=512       ,      lowerCamelCase			:    int=2       ,      lowerCamelCase			:    Union[str, Any]=0.02       ,      lowerCamelCase			:    Any=1E-12       ,      lowerCamelCase			:    Union[str, Any]=0       ,      lowerCamelCase			:    Optional[int]="absolute"       ,      lowerCamelCase			:    List[str]=True       ,      **lowerCamelCase			:    Dict       ,      )      ->       Optional[int]:
														super().__init__(**lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  vocab_size
														__snake_case						:     List[str]									=  hidden_size
														__snake_case						:     Any									=  num_hidden_layers
														__snake_case						:     Optional[Any]									=  num_attention_heads
														__snake_case						:     List[Any]									=  hidden_act
														__snake_case						:     List[Any]									=  intermediate_size
														__snake_case						:     int									=  hidden_dropout_prob
														__snake_case						:     Union[str, Any]									=  attention_probs_dropout_prob
														__snake_case						:     Tuple									=  max_position_embeddings
														__snake_case						:     List[Any]									=  type_vocab_size
														__snake_case						:     List[Any]									=  initializer_range
														__snake_case						:     List[str]									=  layer_norm_eps
														__snake_case						:     List[Any]									=  position_embedding_type
														__snake_case						:     List[Any]									=  use_cache
														__snake_case						:     str									=  pad_token_id






							@classmethod
							def        __snake_case   (				cls			:    List[str]       ,      lowerCamelCase			:    Union[str, os.PathLike]       ,      **lowerCamelCase			:    Any							)      ->       "PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase							)

														__snake_case					,     __snake_case						:     Union[str, Any]									=  cls.get_config_dict(lowerCamelCase       ,      **lowerCamelCase							)

														# get the text config dict if we are loading from AlignConfig
														if config_dict.get("model_type"							) == "align":
																					__snake_case						:     int									=  config_dict["text_config"]

														if "model_type" in config_dict and hasattr(cls       ,      "model_type"							) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																					    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

														return cls.from_dict(lowerCamelCase       ,      **lowerCamelCase							)

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    "align_vision_model"

							def __init__(				self			:    Dict       ,      lowerCamelCase			:    int = 3       ,      lowerCamelCase			:    int = 600       ,      lowerCamelCase			:    float = 2.0       ,      lowerCamelCase			:    float = 3.1       ,      lowerCamelCase			:    int = 8       ,      lowerCamelCase			:    List[int] = [3, 3, 5, 3, 5, 5, 3]       ,      lowerCamelCase			:    List[int] = [32, 16, 24, 40, 80, 112, 192]       ,      lowerCamelCase			:    List[int] = [16, 24, 40, 80, 112, 192, 320]       ,      lowerCamelCase			:    List[int] = []       ,      lowerCamelCase			:    List[int] = [1, 2, 2, 2, 1, 2, 1]       ,      lowerCamelCase			:    List[int] = [1, 2, 2, 3, 3, 4, 1]       ,      lowerCamelCase			:    List[int] = [1, 6, 6, 6, 6, 6, 6]       ,      lowerCamelCase			:    float = 0.25       ,      lowerCamelCase			:    str = "swish"       ,      lowerCamelCase			:    int = 2560       ,      lowerCamelCase			:    str = "mean"       ,      lowerCamelCase			:    float = 0.02       ,      lowerCamelCase			:    float = 0.0_01       ,      lowerCamelCase			:    float = 0.99       ,      lowerCamelCase			:    float = 0.2       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       List[Any]:
														super().__init__(**lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  num_channels
														__snake_case						:     int									=  image_size
														__snake_case						:     Any									=  width_coefficient
														__snake_case						:     List[Any]									=  depth_coefficient
														__snake_case						:     Any									=  depth_divisor
														__snake_case						:     Union[str, Any]									=  kernel_sizes
														__snake_case						:     Union[str, Any]									=  in_channels
														__snake_case						:     int									=  out_channels
														__snake_case						:     Tuple									=  depthwise_padding
														__snake_case						:     List[str]									=  strides
														__snake_case						:     Optional[int]									=  num_block_repeats
														__snake_case						:     Tuple									=  expand_ratios
														__snake_case						:     List[Any]									=  squeeze_expansion_ratio
														__snake_case						:     int									=  hidden_act
														__snake_case						:     int									=  hidden_dim
														__snake_case						:     List[str]									=  pooling_type
														__snake_case						:     Optional[int]									=  initializer_range
														__snake_case						:     str									=  batch_norm_eps
														__snake_case						:     Union[str, Any]									=  batch_norm_momentum
														__snake_case						:     Dict									=  drop_connect_rate
														__snake_case						:     Dict									=  sum(lowerCamelCase							) * 4






							@classmethod
							def        __snake_case   (				cls			:    Any       ,      lowerCamelCase			:    Union[str, os.PathLike]       ,      **lowerCamelCase			:    int							)      ->       "PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase							)

														__snake_case					,     __snake_case						:     List[Any]									=  cls.get_config_dict(lowerCamelCase       ,      **lowerCamelCase							)

														# get the vision config dict if we are loading from AlignConfig
														if config_dict.get("model_type"							) == "align":
																					__snake_case						:     List[str]									=  config_dict["vision_config"]

														if "model_type" in config_dict and hasattr(cls       ,      "model_type"							) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																					    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

														return cls.from_dict(lowerCamelCase       ,      **lowerCamelCase							)

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Dict	    =    "align"
							__UpperCAmelCase       :				Optional[int]	    =    True

							def __init__(				self			:    str       ,      lowerCamelCase			:    List[Any]=None       ,      lowerCamelCase			:    Optional[Any]=None       ,      lowerCamelCase			:    Tuple=640       ,      lowerCamelCase			:    List[Any]=1.0       ,      lowerCamelCase			:    Dict=0.02       ,      **lowerCamelCase			:    Tuple       ,      )      ->       str:
														super().__init__(**lowerCamelCase							)

														if text_config is None:
																					__snake_case						:     Dict									=  {}
																					logger.info("text_config is None. Initializing the AlignTextConfig with default values."							)

														if vision_config is None:
																					__snake_case						:     Union[str, Any]									=  {}
																					logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."							)

														__snake_case						:     Union[str, Any]									=  AlignTextConfig(**lowerCamelCase							)
														__snake_case						:     List[Any]									=  AlignVisionConfig(**lowerCamelCase							)

														__snake_case						:     List[Any]									=  projection_dim
														__snake_case						:     Optional[int]									=  temperature_init_value
														__snake_case						:     Any									=  initializer_range

							@classmethod
							def        __snake_case   (				cls			:    Union[str, Any]       ,      lowerCamelCase			:    AlignTextConfig       ,      lowerCamelCase			:    AlignVisionConfig       ,      **lowerCamelCase			:    Tuple							)      ->       Any:
														return cls(text_config=text_config.to_dict()       ,      vision_config=vision_config.to_dict()       ,      **lowerCamelCase							)






							def        __snake_case   (				self			:    str							)      ->       int:
														__snake_case						:     Optional[int]									=  copy.deepcopy(self.__dict__							)
														__snake_case						:     List[Any]									=  self.text_config.to_dict()
														__snake_case						:     List[Any]									=  self.vision_config.to_dict()
														__snake_case						:     List[str]									=  self.__class__.model_type
														return output


'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase__  :						str					=      get_tests_dir('fixtures')


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	mock.Mock()
         UpperCAmelCase__				:					Dict 					=	500
         UpperCAmelCase__				:					Optional[int] 					=	{}
         UpperCAmelCase__				:					List[str] 					=	HTTPError
         UpperCAmelCase__				:					Tuple 					=	{}

         # Download this model to make sure it's in the cache.
         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch('''requests.Session.request'''       ,return_value=lowerCamelCase_				) as mock_head:
            UpperCAmelCase__				:					Tuple 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
            # This check we did call the fake head request
            mock_head.assert_called()




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	ViTImageProcessor.from_pretrained(
             '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''				)




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         with self.assertRaises(lowerCamelCase_				):
            # config is in subfolder, the following should not work without specifying the subfolder
            UpperCAmelCase__				:					int 					=	AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''				)

         UpperCAmelCase__				:					Optional[Any] 					=	AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/stable-diffusion-all-variants'''       ,subfolder='''feature_extractor'''				)

         self.assertIsNotNone(lowerCamelCase_				)




@is_staging_test
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @classmethod
      def 		lowerCAmelCase__       (					cls				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	TOKEN
         HfFolder.save_token(lowerCamelCase_				)




      @classmethod
      def 		lowerCAmelCase__       (					cls				)      ->      List[str]:



         '''simple docstring'''



         try:
            delete_repo(token=cls._token       ,repo_id='''test-image-processor'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''valid_org/test-image-processor-org'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''test-dynamic-image-processor'''				)
         except HTTPError:
            pass




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''test-image-processor'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					List[Any] 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''valid_org/test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''valid_org/test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''valid_org/test-image-processor-org'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         CustomImageProcessor.register_for_auto_class()
         UpperCAmelCase__				:					List[str] 					=	CustomImageProcessor.from_pretrained(lowerCamelCase_				)

         image_processor.push_to_hub('''test-dynamic-image-processor'''       ,use_auth_token=self._token				)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             image_processor.auto_map       ,{'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}       ,)

         UpperCAmelCase__				:					Any 					=	AutoImageProcessor.from_pretrained(
             f'''{USER}/test-dynamic-image-processor'''       ,trust_remote_code=lowerCamelCase_				)
         # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
         self.assertEqual(new_image_processor.__class__.__name__       ,'''CustomImageProcessor'''				)




