


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




						"""simple docstring"""

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

						return name






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




						"""simple docstring"""

						for key in orig_state_dict.copy().keys():
												snake_case_ =   orig_state_dict.pop(a_      )

												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
																		snake_case_ =   key.split('''.'''      )
																		snake_case_ =   int(key_split[2]      ), int(key_split[4]      )
																		snake_case_ =   config.vision_config.hidden_size
																		if "weight" in key:
																								snake_case_ =   val[:dim, :]
																								snake_case_ =   val[dim : dim * 2, :]
																								snake_case_ =   val[-dim:, :]
																		else:
																								snake_case_ =   val[:dim]
																								snake_case_ =   val[dim : dim * 2]
																								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
																		snake_case_ =   key.split('''.'''      )
																		snake_case_ =   int(key_split[3]      )
																		snake_case_ =   config.text_config.hidden_size
																		if "weight" in key:
																								snake_case_ =   val[:dim, :]
																								snake_case_ =   val[
																								    dim : dim * 2, :
																								]
																								snake_case_ =   val[-dim:, :]
																		else:
																								snake_case_ =   val[:dim]
																								snake_case_ =   val[dim : dim * 2]
																								snake_case_ =   val[-dim:]
												else:
																		snake_case_ =   rename_key(a_      )
																		# 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
																		):
																								snake_case_ =   val.squeeze_()
																		else:
																								snake_case_ =   val

						return orig_state_dict






def  __lowerCAmelCase					()->						List[Any]:




						"""simple docstring"""

						snake_case_ =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case_ =   Image.open(requests.get(a_      ,      stream=a_      ).raw      )
						return im






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"      ,      SCREAMING_SNAKE_CASE=False      )->						Any:




						"""simple docstring"""

						snake_case_ =   GroupViTConfig()
						snake_case_ =   GroupViTModel(a_      ).eval()

						snake_case_ =   torch.load(a_      ,      map_location='''cpu'''      )['''model''']
						snake_case_ =   convert_state_dict(a_      ,      a_      )
						snake_case_ =   model.load_state_dict(a_      ,      strict=a_      )
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a_      ) == 0)

						# verify result
						snake_case_ =   CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
						snake_case_ =   prepare_img()
						snake_case_ =   processor(text=['''a photo of a cat''', '''a photo of a dog''']      ,      images=a_      ,      padding=a_      ,      return_tensors='''pt'''      )

						with torch.no_grad():
												snake_case_ =   model(**a_      )

						if model_name == "groupvit-gcc-yfcc":
												snake_case_ =   torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]      )
						elif model_name == "groupvit-gcc-redcaps":
												snake_case_ =   torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]      )
						else:
												raise ValueError(f'''Model name {model_name} not supported.'''      )
						assert torch.allclose(outputs.logits_per_image      ,      a_      ,      atol=1E-3      )

						processor.save_pretrained(a_      )
						model.save_pretrained(a_      )
						print('''Successfully saved processor and model to'''      ,      a_      )

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


if __name__ == "__main__":
			UpperCAmelCase    =					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`.""",
			)
			UpperCAmelCase    =					parser.parse_args()

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



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

# Symbols
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase    =					symbols("""ct x y z""")






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if velocity > c:
            raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''      )
      elif velocity < 1:
            # Usually the speed should be much higher than 1 (c order of magnitude)
            raise ValueError('''Speed must be greater than or equal to 1!'''      )

      return velocity / c






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE      ) ** 2      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """simple docstring"""

      return np.array(
          [
              [gamma(SCREAMING_SNAKE_CASE      ), -gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), 0, 0],
              [-gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), gamma(SCREAMING_SNAKE_CASE      ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ]      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   np.array([ct, x, y, z]      )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(SCREAMING_SNAKE_CASE      ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   UpperCAmelCase    =					transform(2997_9245)
   print("""Example of four vector: """)
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   UpperCAmelCase    =					{ct: c, x: 1, y: 1, z: 1}
   UpperCAmelCase    =					[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')