

import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase		:   int										=       logging.get_logger(__name__)

_UpperCAmelCase		:   Optional[int]										=       {
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			'''owlvit_text_model'''




				def __init__(					self      ,   snake_case=4_9408      ,   snake_case=512      ,   snake_case=2048      ,   snake_case=12      ,   snake_case=8      ,   snake_case=16      ,   snake_case="quick_gelu"      ,   snake_case=1e-5      ,   snake_case=0.0      ,   snake_case=0.02      ,   snake_case=1.0      ,   snake_case=0      ,   snake_case=4_9406      ,   snake_case=4_9407      ,   **snake_case      ,   ):
											super().__init__(pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   **snake_case       )

											snake_case_							=    vocab_size
											snake_case_							=    hidden_size
											snake_case_							=    intermediate_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    max_position_embeddings
											snake_case_							=    hidden_act
											snake_case_							=    layer_norm_eps
											snake_case_							=    attention_dropout
											snake_case_							=    initializer_range
											snake_case_							=    initializer_factor




				@classmethod
				def     a							(					cls      ,   snake_case      ,   **snake_case       ):
											cls._set_token_in_kwargs(snake_case       )

											snake_case_				,    snake_case_							=    cls.get_config_dict(snake_case      ,   **snake_case       )

											# get the text config dict if we are loading from OwlViTConfig
											if config_dict.get('model_type'       ) == "owlvit":
																		snake_case_							=    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(snake_case      ,   **snake_case       )







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			'''owlvit_vision_model'''




				def __init__(					self      ,   snake_case=768      ,   snake_case=3072      ,   snake_case=12      ,   snake_case=12      ,   snake_case=3      ,   snake_case=768      ,   snake_case=32      ,   snake_case="quick_gelu"      ,   snake_case=1e-5      ,   snake_case=0.0      ,   snake_case=0.02      ,   snake_case=1.0      ,   **snake_case      ,   ):
											super().__init__(**snake_case       )

											snake_case_							=    hidden_size
											snake_case_							=    intermediate_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    num_channels
											snake_case_							=    image_size
											snake_case_							=    patch_size
											snake_case_							=    hidden_act
											snake_case_							=    layer_norm_eps
											snake_case_							=    attention_dropout
											snake_case_							=    initializer_range
											snake_case_							=    initializer_factor




				@classmethod
				def     a							(					cls      ,   snake_case      ,   **snake_case       ):
											cls._set_token_in_kwargs(snake_case       )

											snake_case_				,    snake_case_							=    cls.get_config_dict(snake_case      ,   **snake_case       )

											# get the vision config dict if we are loading from OwlViTConfig
											if config_dict.get('model_type'       ) == "owlvit":
																		snake_case_							=    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(snake_case      ,   **snake_case       )







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			'''owlvit'''
				__SCREAMING_SNAKE_CASE					: Tuple       			=			True




				def __init__(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=512      ,   snake_case=2.65_92      ,   snake_case=True      ,   **snake_case      ,   ):
											super().__init__(**snake_case       )

											if text_config is None:
																		snake_case_							=    {}
																		logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'       )

											if vision_config is None:
																		snake_case_							=    {}
																		logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'       )

											snake_case_							=    OwlViTTextConfig(**snake_case       )
											snake_case_							=    OwlViTVisionConfig(**snake_case       )

											snake_case_							=    projection_dim
											snake_case_							=    logit_scale_init_value
											snake_case_							=    return_dict
											snake_case_							=    1.0




				@classmethod
				def     a							(					cls      ,   snake_case      ,   **snake_case       ):
											cls._set_token_in_kwargs(snake_case       )

											snake_case_				,    snake_case_							=    cls.get_config_dict(snake_case      ,   **snake_case       )

											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(snake_case      ,   **snake_case       )




				@classmethod
				def     a							(					cls      ,   snake_case      ,   snake_case      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    text_config
											snake_case_							=    vision_config

											return cls.from_dict(snake_case      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    copy.deepcopy(self.__dict__       )
											snake_case_							=    self.text_config.to_dict()
											snake_case_							=    self.vision_config.to_dict()
											snake_case_							=    self.__class__.model_type
											return output







class  lowercase				(       lowercase_			):




				@property
				def     a							(					self       ):
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											    ]       )




				@property
				def     a							(					self       ):
											return OrderedDict(
											    [
											        ('logits_per_image', {0: 'batch'}),
											        ('logits_per_text', {0: 'batch'}),
											        ('text_embeds', {0: 'batch'}),
											        ('image_embeds', {0: 'batch'}),
											    ]       )




				@property
				def     a							(					self       ):
											return 1e-4




				def     a							(					self      ,   snake_case      ,   snake_case = -1      ,   snake_case = -1      ,   snake_case = None      ,   ):
											snake_case_							=    super().generate_dummy_inputs(
											    processor.tokenizer      ,   batch_size=snake_case      ,   seq_length=snake_case      ,   framework=snake_case       )
											snake_case_							=    super().generate_dummy_inputs(
											    processor.image_processor      ,   batch_size=snake_case      ,   framework=snake_case       )
											return {**text_input_dict, **image_input_dict}




				@property
				def     a							(					self       ):
											return 14







import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    1.5
							snake_case_							=    int(factor * num_class_images      )
							snake_case_							=    ClipClient(
							    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1      )

							os.makedirs(F'''{class_data_dir}/images'''   ,							exist_ok=UpperCamelCase__      )
							if len(list(Path(F'''{class_data_dir}/images'''      ).iterdir()      )      ) >= num_class_images:
														return

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					snake_case_							=    int(factor * num_images      )
																					snake_case_							=    ClipClient(
																					    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							with open(F'''{class_data_dir}/caption.txt'''   ,							'w'      ) as fa, open(F'''{class_data_dir}/urls.txt'''   ,							'w'      ) as fa, open(
							    F'''{class_data_dir}/images.txt'''   ,							'w'      ) as fa:
														while total < num_class_images:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    Image.open(BytesIO(img.content      )      )
																																			with open(F'''{class_data_dir}/images/{total}.jpg'''   ,							'wb'      ) as f:
																																										f.write(img.content      )
																																			fa.write(images['caption'] + '\n'      )
																																			fa.write(images['url'] + '\n'      )
																																			fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'      )
																																			total += 1
																																			pbar.update(1      )
																												else:
																																			continue
																					except Exception:
																												continue
							return





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(''   ,							add_help=UpperCamelCase__      )
							parser.add_argument('--class_prompt'   ,							help='text prompt to retrieve images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--class_data_dir'   ,							help='path to save images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--num_class_images'   ,							help='number of images to download'   ,							default=200   ,							type=UpperCamelCase__      )
							return parser.parse_args()


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




