
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def   lowerCAmelCase_						(					_snake_case      :  Tuple   )				->		Union[str, Any]:







	'''simple docstring'''


	if "img_encoder.pos_embed" in name:
		__magic_name__		:		Optional[Any]	   =     name.replace("img_encoder.pos_embed"      ,   "vision_model.embeddings.position_embeddings"   )
	if "img_encoder.patch_embed.proj" in name:
		__magic_name__		:		Union[str, Any]	   =     name.replace("img_encoder.patch_embed.proj"      ,   "vision_model.embeddings.patch_embeddings.projection"   )
	if "img_encoder.patch_embed.norm" in name:
		__magic_name__		:		str	   =     name.replace("img_encoder.patch_embed.norm"      ,   "vision_model.embeddings.layernorm"   )
	if "img_encoder.layers" in name:
		__magic_name__		:		Optional[Any]	   =     name.replace("img_encoder.layers"      ,   "vision_model.encoder.stages"   )
	if "blocks" in name and "res" not in name:
		__magic_name__		:		List[Any]	   =     name.replace("blocks"      ,   "layers"   )
	if "attn" in name and "pre_assign" not in name:
		__magic_name__		:		int	   =     name.replace("attn"      ,   "self_attn"   )
	if "proj" in name and "self_attn" in name and "text" not in name:
		__magic_name__		:		int	   =     name.replace("proj"      ,   "out_proj"   )
	if "pre_assign_attn.attn.proj" in name:
		__magic_name__		:		List[Any]	   =     name.replace("pre_assign_attn.attn.proj"      ,   "pre_assign_attn.attn.out_proj"   )
	if "norm1" in name:
		__magic_name__		:		str	   =     name.replace("norm1"      ,   "layer_norm1"   )
	if "norm2" in name and "pre_assign" not in name:
		__magic_name__		:		List[str]	   =     name.replace("norm2"      ,   "layer_norm2"   )
	if "img_encoder.norm" in name:
		__magic_name__		:		Any	   =     name.replace("img_encoder.norm"      ,   "vision_model.layernorm"   )
	# text encoder
	if "text_encoder.token_embedding" in name:
		__magic_name__		:		str	   =     name.replace("text_encoder.token_embedding"      ,   "text_model.embeddings.token_embedding"   )
	if "text_encoder.positional_embedding" in name:
		__magic_name__		:		Optional[int]	   =     name.replace("text_encoder.positional_embedding"      ,   "text_model.embeddings.position_embedding.weight"   )
	if "text_encoder.transformer.resblocks." in name:
		__magic_name__		:		List[str]	   =     name.replace("text_encoder.transformer.resblocks."      ,   "text_model.encoder.layers."   )
	if "ln_1" in name:
		__magic_name__		:		Dict	   =     name.replace("ln_1"      ,   "layer_norm1"   )
	if "ln_2" in name:
		__magic_name__		:		Any	   =     name.replace("ln_2"      ,   "layer_norm2"   )
	if "c_fc" in name:
		__magic_name__		:		Dict	   =     name.replace("c_fc"      ,   "fc1"   )
	if "c_proj" in name:
		__magic_name__		:		Union[str, Any]	   =     name.replace("c_proj"      ,   "fc2"   )
	if "text_encoder" in name:
		__magic_name__		:		Union[str, Any]	   =     name.replace("text_encoder"      ,   "text_model"   )
	if "ln_final" in name:
		__magic_name__		:		Optional[int]	   =     name.replace("ln_final"      ,   "final_layer_norm"   )
	# projection layers
	if "img_projector.linear_hidden." in name:
		__magic_name__		:		int	   =     name.replace("img_projector.linear_hidden."      ,   "visual_projection."   )
	if "img_projector.linear_out." in name:
		__magic_name__		:		str	   =     name.replace("img_projector.linear_out."      ,   "visual_projection.3."   )
	if "text_projector.linear_hidden" in name:
		__magic_name__		:		Any	   =     name.replace("text_projector.linear_hidden"      ,   "text_projection"   )
	if "text_projector.linear_out" in name:
		__magic_name__		:		Any	   =     name.replace("text_projector.linear_out"      ,   "text_projection.3"   )

	return name






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Any   )				->		Optional[Any]:







	'''simple docstring'''


	for key in orig_state_dict.copy().keys():
		__magic_name__		:		Tuple	   =     orig_state_dict.pop(UpperCamelCase__   )

		if "qkv" in key:
			# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			__magic_name__		:		Tuple	   =     key.split("."   )
			__magic_name__   ,    __magic_name__		:		List[str]	   =     int(key_split[2]   ), int(key_split[4]   )
			__magic_name__		:		Optional[int]	   =     config.vision_config.hidden_size
			if "weight" in key:
				__magic_name__		:		Optional[int]	   =     val[:dim, :]
				__magic_name__		:		List[Any]	   =     val[dim : dim * 2, :]
				__magic_name__		:		Optional[int]	   =     val[-dim:, :]
			else:
				__magic_name__		:		Dict	   =     val[:dim]
				__magic_name__		:		Optional[int]	   =     val[dim : dim * 2]
				__magic_name__		:		List[str]	   =     val[-dim:]
		elif "in_proj" in key:
			# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
			# we need to split them up into separate matrices/vectors
			__magic_name__		:		int	   =     key.split("."   )
			__magic_name__		:		List[str]	   =     int(key_split[3]   )
			__magic_name__		:		Optional[Any]	   =     config.text_config.hidden_size
			if "weight" in key:
				__magic_name__		:		Dict	   =     val[:dim, :]
				__magic_name__		:		Tuple	   =     val[
				    dim : dim * 2, :
				]
				__magic_name__		:		List[Any]	   =     val[-dim:, :]
			else:
				__magic_name__		:		Union[str, Any]	   =     val[:dim]
				__magic_name__		:		Optional[int]	   =     val[dim : dim * 2]
				__magic_name__		:		Dict	   =     val[-dim:]
		else:
			__magic_name__		:		Optional[Any]	   =     rename_key(UpperCamelCase__   )
			# squeeze if necessary
			if (
			    "text_projection.0" in new_name
			    or "text_projection.3" in new_name
			    or "visual_projection.0" in new_name
			    or "visual_projection.3" in new_name
			):
				__magic_name__		:		Optional[int]	   =     val.squeeze_()
			else:
				__magic_name__		:		Dict	   =     val

	return orig_state_dict






def   lowerCAmelCase_						(					)				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		str	   =     Image.open(requests.get(UpperCamelCase__      ,   stream=UpperCamelCase__   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  str="groupvit-gcc-yfcc"      ,   _snake_case      :  str=False   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     GroupViTConfig()
	__magic_name__		:		Tuple	   =     GroupViTModel(UpperCamelCase__   ).eval()

	__magic_name__		:		str	   =     torch.load(UpperCamelCase__      ,   map_location="cpu"   )["model"]
	__magic_name__		:		int	   =     convert_state_dict(UpperCamelCase__      ,   UpperCamelCase__   )
	__magic_name__   ,    __magic_name__		:		Optional[int]	   =     model.load_state_dict(UpperCamelCase__      ,   strict=UpperCamelCase__   )
	assert missing_keys == ["text_model.embeddings.position_ids"]
	assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase__   ) == 0)

	# verify result
	__magic_name__		:		int	   =     CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"   )
	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     processor(text=["a photo of a cat", "a photo of a dog"]      ,   images=UpperCamelCase__      ,   padding=UpperCamelCase__      ,   return_tensors="pt"   )

	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(**UpperCamelCase__   )

	if model_name == "groupvit-gcc-yfcc":
		__magic_name__		:		str	   =     torch.tensor([[13.3_523, 6.3_629]]   )
	elif model_name == "groupvit-gcc-redcaps":
		__magic_name__		:		List[Any]	   =     torch.tensor([[16.1_873, 8.6_230]]   )
	else:
		raise ValueError(F'''Model name {model_name} not supported.'''   )
	assert torch.allclose(outputs.logits_per_image      ,   UpperCamelCase__      ,   atol=1E-3   )

	processor.save_pretrained(UpperCamelCase__   )
	model.save_pretrained(UpperCamelCase__   )
	print("Successfully saved processor and model to"      ,   UpperCamelCase__   )

	if push_to_hub:
		print("Pushing to the hub..."   )
		processor.push_to_hub(UpperCamelCase__      ,   organization="nielsr"   )
		model.push_to_hub(UpperCamelCase__      ,   organization="nielsr"   )


if __name__ == "__main__":
				snake_case :		str							      =  argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
				)
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
				parser.add_argument(
				    "--model_name",
				    default="groupvit-gccy-fcc",
				    type=str,
				    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
				)
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
				)
				snake_case :		str							      =  parser.parse_args()

				convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



def 					__lowerCamelCase	( ):







							'''simple docstring'''






							return [list(range(1000 - i   ,							-1000 - i   ,							-1      )      ) for i in range(1000      )]


_UpperCAmelCase		:   Union[str, Any]										=       generate_large_matrix()
_UpperCAmelCase		:   Tuple										=       (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							assert all(row == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for row in grid      )
							assert all(list(UpperCamelCase__      ) == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for col in zip(*UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(UpperCamelCase__      ) - 1

							# Edge cases such as no values or all numbers are negative.
							if not array or array[0] < 0:
														return 0

							while right + 1 > left:
														snake_case_							=    (left + right) // 2
														snake_case_							=    array[mid]

														# Num must be negative and the index must be greater than or equal to 0.
														if num < 0 and array[mid - 1] >= 0:
																					return mid

														if num >= 0:
																					snake_case_							=    mid + 1
														else:
																					snake_case_							=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(grid[0]      )

							for i in range(len(UpperCamelCase__      )      ):
														snake_case_							=    find_negative_index(grid[i][:bound]      )
														total += bound
							return (len(UpperCamelCase__      ) * len(grid[0]      )) - total





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return len([number for row in grid for number in row if number < 0]      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							for row in grid:
														for i, number in enumerate(UpperCamelCase__      ):
																					if number < 0:
																												total += len(UpperCamelCase__      ) - i
																												break
							return total





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							from timeit import timeit

							print('Running benchmarks'      )
							snake_case_							=    (
							    'from __main__ import count_negatives_binary_search, '
							    'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
							)
							for func in (
							    "count_negatives_binary_search",  # took 0.7727 seconds
							    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
							    "count_negatives_brute_force",  # took 12.8160 seconds
							):
														snake_case_							=    timeit(F'''{func}(grid=grid)'''   ,							setup=UpperCamelCase__   ,							number=500      )
														print(F'''{func}() took {time:0.4f} seconds'''      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()




