'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


_lowercase		:				List[str]	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, Any]	       =		{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''perceiver'''







			def __init__(			self       :						str       ,    lowercase_       :						Union[str, Any]=256       ,    lowercase_       :						Dict=1280       ,    lowercase_       :						List[str]=768       ,    lowercase_       :						int=1       ,    lowercase_       :						Any=26       ,    lowercase_       :						Tuple=8       ,    lowercase_       :						Any=8       ,    lowercase_       :						int=None       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						Union[str, Any]="kv"       ,    lowercase_       :						Optional[int]=1       ,    lowercase_       :						str=1       ,    lowercase_       :						Tuple="gelu"       ,    lowercase_       :						Optional[int]=0.1       ,    lowercase_       :						Dict=0.02       ,    lowercase_       :						Optional[int]=1E-12       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						List[str]=262       ,    lowercase_       :						Tuple=2048       ,    lowercase_       :						int=56       ,    lowercase_       :						int=[368, 496]       ,    lowercase_       :						Tuple=16       ,    lowercase_       :						List[Any]=1920       ,    lowercase_       :						Optional[Any]=16       ,    lowercase_       :						Tuple=[1, 16, 224, 224]       ,    **lowercase_       :						Tuple       ,    ):
										super().__init__(**lowercase_					)

										lowercase_      :    Any       		= num_latents
										lowercase_      :    Dict       		= d_latents
										lowercase_      :    Tuple       		= d_model
										lowercase_      :    List[Any]       		= num_blocks
										lowercase_      :    Optional[Any]       		= num_self_attends_per_block
										lowercase_      :    Tuple       		= num_self_attention_heads
										lowercase_      :    Any       		= num_cross_attention_heads
										lowercase_      :    Union[str, Any]       		= qk_channels
										lowercase_      :    Union[str, Any]       		= v_channels
										lowercase_      :    Optional[Any]       		= cross_attention_shape_for_attention
										lowercase_      :    Any       		= self_attention_widening_factor
										lowercase_      :    int       		= cross_attention_widening_factor
										lowercase_      :    Optional[int]       		= hidden_act
										lowercase_      :    Any       		= attention_probs_dropout_prob
										lowercase_      :    int       		= initializer_range
										lowercase_      :    int       		= layer_norm_eps
										lowercase_      :    Optional[int]       		= use_query_residual
										# masked language modeling attributes
										lowercase_      :    Dict       		= vocab_size
										lowercase_      :    Dict       		= max_position_embeddings
										# image classification attributes
										lowercase_      :    Any       		= image_size
										# flow attributes
										lowercase_      :    str       		= train_size
										# multimodal autoencoding attributes
										lowercase_      :    Dict       		= num_frames
										lowercase_      :    Optional[Any]       		= audio_samples_per_frame
										lowercase_      :    Optional[Any]       		= samples_per_patch
										lowercase_      :    int       		= output_shape





class 					__magic_name__		(					_UpperCAmelCase):







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										if self.task == "multiple-choice":
																	lowercase_      :    Dict       		= {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																	lowercase_      :    int       		= {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""inputs""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										return 1E-4







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,    lowercase_       :						int = -1       ,    lowercase_       :						int = -1       ,    lowercase_       :						int = -1       ,    lowercase_       :						bool = False       ,    lowercase_       :						Optional[TensorType] = None       ,    lowercase_       :						int = 3       ,    lowercase_       :						int = 40       ,    lowercase_       :						int = 40       ,    ):
										# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

										if isinstance(lowercase_       ,    lowercase_					):
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	lowercase_      :    Dict       		= compute_effective_axis_dimension(
																	    lowercase_       ,    fixed_dimension=OnnxConfig.default_fixed_batch       ,    num_token_to_add=0					)
																	# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																	lowercase_      :    int       		= preprocessor.num_special_tokens_to_add(lowercase_					)
																	lowercase_      :    Optional[Any]       		= compute_effective_axis_dimension(
																	    lowercase_       ,    fixed_dimension=OnnxConfig.default_fixed_sequence       ,    num_token_to_add=lowercase_					)
																	# Generate dummy inputs according to compute batch and sequence
																	lowercase_      :    Optional[int]       		= [""" """.join(["""a"""]					) * seq_length] * batch_size
																	lowercase_      :    List[Any]       		= dict(preprocessor(lowercase_       ,    return_tensors=lowercase_					)					)
																	lowercase_      :    List[Any]       		= inputs.pop("""input_ids"""					)
																	return inputs
										elif isinstance(lowercase_       ,    lowercase_					) and preprocessor.model_input_names[0] == "pixel_values":
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	lowercase_      :    int       		= compute_effective_axis_dimension(lowercase_       ,    fixed_dimension=OnnxConfig.default_fixed_batch					)
																	lowercase_      :    Union[str, Any]       		= self._generate_dummy_images(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	lowercase_      :    List[Any]       		= dict(preprocessor(images=lowercase_       ,    return_tensors=lowercase_					)					)
																	lowercase_      :    str       		= inputs.pop("""pixel_values"""					)
																	return inputs
										else:
																	raise ValueError(
																	    """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""					)






'''simple docstring'''




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__ :      Union[str, Any]   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   List[Any]:
							lowercase_      :    str       		= 1.5
							lowercase_      :    List[Any]       		= int(factor * num_class_images				)
							lowercase_      :    int       		= 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:
														lowercase_      :    List[str]       		= client.query(text=UpperCAmelCase__				)
														if len(UpperCAmelCase__				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					lowercase_      :    List[str]       		= int(factor * num_images				)
																					lowercase_      :    List[str]       		= ClipClient(
																					    url="""https://knn.laion.ai/knn-service"""   ,   indice_name="""laion_400m"""   ,   num_images=UpperCAmelCase__   ,   aesthetic_weight=0.1   ,   )

							lowercase_      :    List[str]       		= 0
							lowercase_      :    Dict       		= 0
							lowercase_      :    Tuple       		= 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:
																					lowercase_      :    str       		= class_images[count]
																					count += 1
																					try:
																												lowercase_      :    Union[str, Any]       		= requests.get(images["""url"""]				)
																												if img.status_code == 200:
																																			lowercase_      :    List[str]       		= 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			(   )    ->   Optional[Any]:
							lowercase_      :    Any       		= 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__":
						_lowercase		:				Dict	       =		parse_args()
						retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)





