






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 					lowerCamelCase__					(_UpperCAmelCase):


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

			return name






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):


			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE							=			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
									SCREAMING_SNAKE_CASE							=			key.split('.')
									SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			int(key_split[2]), int(key_split[4])
									SCREAMING_SNAKE_CASE							=			config.vision_config.hidden_size
									if "weight" in key:
												SCREAMING_SNAKE_CASE							=			val[:dim, :]
												SCREAMING_SNAKE_CASE							=			val[dim : dim * 2, :]
												SCREAMING_SNAKE_CASE							=			val[-dim:, :]
									else:
												SCREAMING_SNAKE_CASE							=			val[:dim]
												SCREAMING_SNAKE_CASE							=			val[dim : dim * 2]
												SCREAMING_SNAKE_CASE							=			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
									SCREAMING_SNAKE_CASE							=			key.split('.')
									SCREAMING_SNAKE_CASE							=			int(key_split[3])
									SCREAMING_SNAKE_CASE							=			config.text_config.hidden_size
									if "weight" in key:
												SCREAMING_SNAKE_CASE							=			val[:dim, :]
												SCREAMING_SNAKE_CASE							=			val[
												    dim : dim * 2, :
												]
												SCREAMING_SNAKE_CASE							=			val[-dim:, :]
									else:
												SCREAMING_SNAKE_CASE							=			val[:dim]
												SCREAMING_SNAKE_CASE							=			val[dim : dim * 2]
												SCREAMING_SNAKE_CASE							=			val[-dim:]
						else:
									SCREAMING_SNAKE_CASE							=			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
									):
												SCREAMING_SNAKE_CASE							=			val.squeeze_()
									else:
												SCREAMING_SNAKE_CASE							=			val

			return orig_state_dict






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE							=			Image.open(requests.get(UpperCamelCase__				,							stream=UpperCamelCase__).raw)
			return im






@torch.no_grad()
def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase="groupvit-gcc-yfcc"				,							_UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE							=			GroupViTConfig()
			SCREAMING_SNAKE_CASE							=			GroupViTModel(UpperCamelCase__).eval()

			SCREAMING_SNAKE_CASE							=			torch.load(UpperCamelCase__				,							map_location='cpu')['model']
			SCREAMING_SNAKE_CASE							=			convert_state_dict(UpperCamelCase__				,							UpperCamelCase__)
			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			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
			SCREAMING_SNAKE_CASE							=			CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
			SCREAMING_SNAKE_CASE							=			prepare_img()
			SCREAMING_SNAKE_CASE							=			processor(text=['a photo of a cat', 'a photo of a dog']				,							images=UpperCamelCase__				,							padding=UpperCamelCase__				,							return_tensors='pt')

			with torch.no_grad():
						SCREAMING_SNAKE_CASE							=			model(**UpperCamelCase__)

			if model_name == "groupvit-gcc-yfcc":
						SCREAMING_SNAKE_CASE							=			torch.tensor([[13.35_23, 6.36_29]])
			elif model_name == "groupvit-gcc-redcaps":
						SCREAMING_SNAKE_CASE							=			torch.tensor([[16.18_73, 8.62_30]])
			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__":
				a_		:    Any   							=     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`.',
				)
				a_		:    List[Any]   							=     parser.parse_args()

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










from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


