
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ImageGPTImageProcessor





class 	_lowerCAmelCase					(	unittest.TestCase       ):




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=7				,				_UpperCamelCase=3				,				_UpperCamelCase=18				,				_UpperCamelCase=30				,				_UpperCamelCase=400				,				_UpperCamelCase=True				,				_UpperCamelCase=None				,				_UpperCamelCase=True				,				)				->      List[Any]:
							lowerCAmelCase_						=	size if size is not None else {"""height""": 18, """width""": 18}
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	num_channels
							lowerCAmelCase_						=	image_size
							lowerCAmelCase_						=	min_resolution
							lowerCAmelCase_						=	max_resolution
							lowerCAmelCase_						=	do_resize
							lowerCAmelCase_						=	size
							lowerCAmelCase_						=	do_normalize




				def      __a						(					self     )				->      Optional[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
							            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
							        ]     ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}





@require_torch
@require_vision
class 	_lowerCAmelCase					(	__SCREAMING_SNAKE_CASE					,      unittest.TestCase       ):
				_lowercase					=ImageGPTImageProcessor if is_vision_available() else None




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	ImageGPTImageProcessingTester(self     )




				@property
				def      __a						(					self     )				->      Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(_UpperCamelCase				,				"clusters"     )     )
							self.assertTrue(hasattr(_UpperCamelCase				,				"do_resize"     )     )
							self.assertTrue(hasattr(_UpperCamelCase				,				"size"     )     )
							self.assertTrue(hasattr(_UpperCamelCase				,				"do_normalize"     )     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.image_processing_class.from_dict(self.image_processor_dict     )
							self.assertEqual(image_processor.size				,				{"height": 18, "width": 18}     )

							lowerCAmelCase_						=	self.image_processing_class.from_dict(self.image_processor_dict				,				size=42     )
							self.assertEqual(image_processor.size				,				{"height": 42, "width": 42}     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.image_processing_class(**self.image_processor_dict     )
							lowerCAmelCase_						=	json.loads(image_processor.to_json_string()     )
							for key, value in self.image_processor_dict.items():
										if key == "clusters":
													self.assertTrue(np.array_equal(_UpperCamelCase				,				obj[key]     )     )
										else:
													self.assertEqual(obj[key]				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.image_processing_class(**self.image_processor_dict     )

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowerCAmelCase_						=	os.path.join(_UpperCamelCase				,				"image_processor.json"     )
										image_processor_first.to_json_file(_UpperCamelCase     )
										lowerCAmelCase_						=	self.image_processing_class.from_json_file(_UpperCamelCase     ).to_dict()

							lowerCAmelCase_						=	image_processor_first.to_dict()
							for key, value in image_processor_first.items():
										if key == "clusters":
													self.assertTrue(np.array_equal(_UpperCamelCase				,				image_processor_second[key]     )     )
										else:
													self.assertEqual(image_processor_first[key]				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.image_processing_class(**self.image_processor_dict     )

							with tempfile.TemporaryDirectory() as tmpdirname:
										image_processor_first.save_pretrained(_UpperCamelCase     )
										lowerCAmelCase_						=	self.image_processing_class.from_pretrained(_UpperCamelCase     ).to_dict()

							lowerCAmelCase_						=	image_processor_first.to_dict()
							for key, value in image_processor_first.items():
										if key == "clusters":
													self.assertTrue(np.array_equal(_UpperCamelCase				,				image_processor_second[key]     )     )
										else:
													self.assertEqual(image_processor_first[key]				,				_UpperCamelCase     )




				@unittest.skip("ImageGPT requires clusters at initialization"     )
				def      __a						(					self     )				->      str:
							pass






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	load_dataset("hf-internal-testing/fixtures_image_utils"    ,							split="test"				)

			lowerCAmelCase_						=	Image.open(dataset[4]["file"]				)
			lowerCAmelCase_						=	Image.open(dataset[5]["file"]				)

			lowerCAmelCase_						=	[imagea, imagea]

			return images





@require_vision
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@slow
				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"     )

							lowerCAmelCase_						=	prepare_images()

							# test non-batched
							lowerCAmelCase_						=	image_processing(images[0]				,				return_tensors="pt"     )

							self.assertIsInstance(encoding.input_ids				,				torch.LongTensor     )
							self.assertEqual(encoding.input_ids.shape				,				(1, 1_024)     )

							lowerCAmelCase_						=	[306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()				,				_UpperCamelCase     )

							# test batched
							lowerCAmelCase_						=	image_processing(_UpperCamelCase				,				return_tensors="pt"     )

							self.assertIsInstance(encoding.input_ids				,				torch.LongTensor     )
							self.assertEqual(encoding.input_ids.shape				,				(2, 1_024)     )

							lowerCAmelCase_						=	[303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()				,				_UpperCamelCase     )





import copy
import os
from typing import Union

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	str							=      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(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_act
       lowerCamelCase		:	Optional[int]							=      initializer_factor
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	Union[str, Any]							=      type_vocab_size
       lowerCamelCase		:	Optional[int]							=      layer_norm_eps
       lowerCamelCase		:	Optional[int]							=      position_embedding_type
       lowerCamelCase		:	List[str]							=      use_cache
       lowerCamelCase		:	List[str]							=      pad_token_id
       lowerCamelCase		:	List[str]							=      bos_token_id
       lowerCamelCase		:	Optional[int]							=      eos_token_id






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[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(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




