





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   int           =  XCLIPTextConfig()

				# derive patch size from model name
				snake_case_						:   Tuple           =  model_name.find('''patch'''			)
				snake_case_						:   Any           =  int(model_name[start_idx + len('''patch'''			) : start_idx + len('''patch'''			) + 2]			)
				snake_case_						:   Any           =  XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE__			,    num_frames=SCREAMING_SNAKE_CASE__			)

				if "large" in model_name:
								snake_case_						:   Optional[Any]           =  768
								snake_case_						:   int           =  3_072
								snake_case_						:   str           =  12

								snake_case_						:   Any           =  1_024
								snake_case_						:   List[Any]           =  4_096
								snake_case_						:   List[Any]           =  16
								snake_case_						:   str           =  24
								snake_case_						:   List[str]           =  768
								snake_case_						:   Optional[Any]           =  3_072

				if model_name == "xclip-large-patch14-16-frames":
								snake_case_						:   str           =  336

				snake_case_						:   int           =  XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

				if "large" in model_name:
								snake_case_						:   List[str]           =  768

				return config






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				if name == "token_embedding.weight":
								snake_case_						:   Union[str, Any]           =  name.replace('''token_embedding.weight'''			,    '''text_model.embeddings.token_embedding.weight'''			)
				if name == "positional_embedding":
								snake_case_						:   List[Any]           =  name.replace('''positional_embedding'''			,    '''text_model.embeddings.position_embedding.weight'''			)
				if "ln_1" in name:
								snake_case_						:   str           =  name.replace('''ln_1'''			,    '''layer_norm1'''			)
				if "ln_2" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''ln_2'''			,    '''layer_norm2'''			)
				if "c_fc" in name:
								snake_case_						:   List[Any]           =  name.replace('''c_fc'''			,    '''fc1'''			)
				if "c_proj" in name:
								snake_case_						:   int           =  name.replace('''c_proj'''			,    '''fc2'''			)
				if name.startswith('''transformer.resblocks'''			):
								snake_case_						:   List[Any]           =  name.replace('''transformer.resblocks'''			,    '''text_model.encoder.layers'''			)
				if "attn.out_proj" in name and "message" not in name:
								snake_case_						:   List[Any]           =  name.replace('''attn.out_proj'''			,    '''self_attn.out_proj'''			)
				if "ln_final" in name:
								snake_case_						:   Tuple           =  name.replace('''ln_final'''			,    '''text_model.final_layer_norm'''			)
				# visual encoder
				if name == "visual.class_embedding":
								snake_case_						:   str           =  name.replace('''visual.class_embedding'''			,    '''vision_model.embeddings.class_embedding'''			)
				if name == "visual.positional_embedding":
								snake_case_						:   int           =  name.replace('''visual.positional_embedding'''			,    '''vision_model.embeddings.position_embedding.weight'''			)
				if name.startswith('''visual.transformer.resblocks'''			):
								snake_case_						:   Union[str, Any]           =  name.replace('''visual.transformer.resblocks'''			,    '''vision_model.encoder.layers'''			)
				if "visual.conv1" in name:
								snake_case_						:   List[str]           =  name.replace('''visual.conv1'''			,    '''vision_model.embeddings.patch_embedding'''			)
				if "visual.ln_pre" in name:
								snake_case_						:   Tuple           =  name.replace('''visual.ln_pre'''			,    '''vision_model.pre_layernorm'''			)
				if "visual.ln_post" in name:
								snake_case_						:   Dict           =  name.replace('''visual.ln_post'''			,    '''vision_model.post_layernorm'''			)
				if "visual.proj" in name:
								snake_case_						:   List[str]           =  name.replace('''visual.proj'''			,    '''visual_projection.weight'''			)
				if "text_projection" in name:
								snake_case_						:   Tuple           =  name.replace('''text_projection'''			,    '''text_projection.weight'''			)
				# things on top
				if "prompts_visual_proj" in name:
								snake_case_						:   Any           =  name.replace('''prompts_visual_proj'''			,    '''prompts_visual_projection'''			)
				if "prompts_visual_ln" in name:
								snake_case_						:   Any           =  name.replace('''prompts_visual_ln'''			,    '''prompts_visual_layernorm'''			)
				# mit
				if name == "mit.positional_embedding":
								snake_case_						:   int           =  name.replace('''positional'''			,    '''position'''			)
				if name.startswith('''mit.resblocks'''			):
								snake_case_						:   Union[str, Any]           =  name.replace('''mit.resblocks'''			,    '''mit.encoder.layers'''			)
				# prompts generator
				if name.startswith('''prompts_generator.norm'''			):
								snake_case_						:   List[Any]           =  name.replace('''prompts_generator.norm'''			,    '''prompts_generator.layernorm'''			)

				return name






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


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

								if "attn.in_proj" in key:
												snake_case_						:   Optional[int]           =  key.split('''.'''			)
												if key.startswith('''visual'''			):
																snake_case_						:   str           =  key_split[3]
																snake_case_						:   Union[str, Any]           =  config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								snake_case_						:   Dict           =  val[
																								    :dim, :
																								]
																								snake_case_						:   Tuple           =  val[
																								    dim : dim * 2, :
																								]
																								snake_case_						:   Any           =  val[
																								    -dim:, :
																								]
																				else:
																								snake_case_						:   Optional[Any]           =  val[
																								    :dim
																								]
																								snake_case_						:   int           =  val[
																								    dim : dim * 2
																								]
																								snake_case_						:   List[Any]           =  val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								snake_case_						:   Any           =  val[
																								    :dim, :
																								]
																								snake_case_						:   Dict           =  val[
																								    dim : dim * 2, :
																								]
																								snake_case_						:   str           =  val[
																								    -dim:, :
																								]
																				else:
																								snake_case_						:   Dict           =  val[:dim]
																								snake_case_						:   Tuple           =  val[
																								    dim : dim * 2
																								]
																								snake_case_						:   Optional[int]           =  val[-dim:]
												elif key.startswith('''mit'''			):
																snake_case_						:   Union[str, Any]           =  key_split[2]
																snake_case_						:   List[str]           =  config.vision_config.mit_hidden_size
																if "weight" in key:
																				snake_case_						:   Tuple           =  val[:dim, :]
																				snake_case_						:   List[Any]           =  val[dim : dim * 2, :]
																				snake_case_						:   Tuple           =  val[-dim:, :]
																else:
																				snake_case_						:   Optional[int]           =  val[:dim]
																				snake_case_						:   int           =  val[dim : dim * 2]
																				snake_case_						:   int           =  val[-dim:]
												else:
																snake_case_						:   int           =  key_split[2]
																snake_case_						:   Any           =  config.text_config.hidden_size
																if "weight" in key:
																				snake_case_						:   Any           =  val[:dim, :]
																				snake_case_						:   Any           =  val[
																				    dim : dim * 2, :
																				]
																				snake_case_						:   Optional[int]           =  val[-dim:, :]
																else:
																				snake_case_						:   Union[str, Any]           =  val[:dim]
																				snake_case_						:   str           =  val[
																				    dim : dim * 2
																				]
																				snake_case_						:   Tuple           =  val[-dim:]
								else:
												snake_case_						:   List[Any]           =  rename_key(SCREAMING_SNAKE_CASE__			)
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																snake_case_						:   Any           =  val.T
												snake_case_						:   int           =  val

				return orig_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				if num_frames == 8:
								snake_case_						:   Optional[Any]           =  '''eating_spaghetti_8_frames.npy'''
				elif num_frames == 16:
								snake_case_						:   List[Any]           =  '''eating_spaghetti.npy'''
				elif num_frames == 32:
								snake_case_						:   Optional[Any]           =  '''eating_spaghetti_32_frames.npy'''
				snake_case_						:   Tuple           =  hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''			,    filename=SCREAMING_SNAKE_CASE__			,    repo_type='''dataset'''			,    )
				snake_case_						:   List[Any]           =  np.load(SCREAMING_SNAKE_CASE__			)
				return list(SCREAMING_SNAKE_CASE__			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=False			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  {
				    # fully supervised kinetics-400 checkpoints
				    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
				    '''xclip-base-patch32-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
				    ),
				    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
				    '''xclip-base-patch16-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
				    ),
				    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
				    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
				    # fully supervised kinetics-600 checkpoints
				    '''xclip-base-patch16-kinetics-600''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
				    ),
				    '''xclip-base-patch16-kinetics-600-16-frames''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
				    ),
				    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
				    # few shot
				    '''xclip-base-patch16-hmdb-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
				    ),
				    '''xclip-base-patch16-hmdb-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
				    ),
				    '''xclip-base-patch16-ucf-2-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
				    ),
				    '''xclip-base-patch16-ucf-4-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
				    ),
				    '''xclip-base-patch16-ucf-8-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
				    ),
				    '''xclip-base-patch16-ucf-16-shot''': (
				        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
				    ),
				    # zero shot
				    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
				}

				snake_case_						:   Tuple           =  model_to_url[model_name]
				snake_case_						:   List[str]           =  8
				if "16-frames" in model_name:
								snake_case_						:   Tuple           =  16
				elif "shot" in model_name:
								snake_case_						:   Optional[int]           =  32

				snake_case_						:   Optional[int]           =  get_xclip_config(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
				snake_case_						:   Union[str, Any]           =  XCLIPModel(SCREAMING_SNAKE_CASE__			)
				model.eval()

				if "drive" in checkpoint_url:
								snake_case_						:   List[Any]           =  '''pytorch_model.bin'''
								gdown.cached_download(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    quiet=SCREAMING_SNAKE_CASE__			)
								snake_case_						:   str           =  torch.load(SCREAMING_SNAKE_CASE__			,    map_location='''cpu'''			)['''model''']
				else:
								snake_case_						:   List[str]           =  torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			)['''model''']

				snake_case_						:   Dict           =  convert_state_dict(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

				snake_case_						:   Union[str, Any]           =  XCLIPModel(SCREAMING_SNAKE_CASE__			)
				snake_case_						:   Dict           =  model.load_state_dict(SCREAMING_SNAKE_CASE__			,    strict=SCREAMING_SNAKE_CASE__			)
				assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
				model.eval()

				snake_case_						:   Optional[Any]           =  336 if model_name == '''xclip-large-patch14-16-frames''' else 224
				snake_case_						:   Union[str, Any]           =  VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE__			)
				snake_case_						:   Optional[Any]           =  CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			)
				snake_case_						:   Union[str, Any]           =  CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''			)
				snake_case_						:   str           =  XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE__			,    tokenizer=SCREAMING_SNAKE_CASE__			)

				snake_case_						:   Tuple           =  prepare_video(SCREAMING_SNAKE_CASE__			)
				snake_case_						:   List[Any]           =  processor(
				    text=['''playing sports''', '''eating spaghetti''', '''go shopping''']			,    videos=SCREAMING_SNAKE_CASE__			,    return_tensors='''pt'''			,    padding=SCREAMING_SNAKE_CASE__			)

				print('''Shape of pixel values:'''			,    inputs.pixel_values.shape			)

				with torch.no_grad():
								snake_case_						:   Optional[Any]           =  model(**SCREAMING_SNAKE_CASE__			)

				# Verify outputs
				snake_case_						:   Tuple           =  outputs.logits_per_video
				snake_case_						:   str           =  logits_per_video.softmax(dim=1			)
				print('''Probs:'''			,    SCREAMING_SNAKE_CASE__			)
				# kinetics-400
				if model_name == "xclip-base-patch32":
								snake_case_						:   Optional[int]           =  torch.tensor([[0.0_019, 0.9_951, 0.0_030]]			)
				elif model_name == "xclip-base-patch32-16-frames":
								snake_case_						:   Optional[Any]           =  torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]			)
				elif model_name == "xclip-base-patch16":
								snake_case_						:   List[Any]           =  torch.tensor([[0.0_083, 0.9_681, 0.0_236]]			)
				elif model_name == "xclip-base-patch16-16-frames":
								snake_case_						:   Optional[int]           =  torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]			)
				elif model_name == "xclip-large-patch14":
								snake_case_						:   Dict           =  torch.tensor([[0.0_062, 0.9_864, 0.0_075]]			)
				elif model_name == "xclip-large-patch14-16-frames":
								snake_case_						:   Tuple           =  torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]			)
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								snake_case_						:   Dict           =  torch.tensor([[0.0_555, 0.8_914, 0.0_531]]			)
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								snake_case_						:   Dict           =  torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]			)
				elif model_name == "xclip-large-patch14-kinetics-600":
								snake_case_						:   str           =  torch.tensor([[0.0_036, 0.9_920, 0.0_045]]			)
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								snake_case_						:   Tuple           =  torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]			)
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								snake_case_						:   Any           =  torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]			)
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								snake_case_						:   Union[str, Any]           =  torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]			)
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								snake_case_						:   List[str]           =  torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]			)
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								snake_case_						:   List[str]           =  torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]			)
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								snake_case_						:   int           =  torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]			)
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								snake_case_						:   Optional[Any]           =  torch.tensor([[0.0_027, 0.9_904, 0.0_070]]			)
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								snake_case_						:   Optional[Any]           =  torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]			)
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								snake_case_						:   int           =  torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]			)
				else:
								raise ValueError(f'''Model name {model_name} not supported'''			)
				assert torch.allclose(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    atol=1E-3			)
				print('''Looks ok!'''			)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
								model.save_pretrained(SCREAMING_SNAKE_CASE__			)

				if push_to_hub:
								print('''Pushing model, processor and slow tokenizer files to the hub...'''			)
								model.push_to_hub(SCREAMING_SNAKE_CASE__			,    organization='''nielsr'''			)
								processor.push_to_hub(SCREAMING_SNAKE_CASE__			,    organization='''nielsr'''			)
								slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE__			,    organization='''nielsr'''			)


if __name__ == "__main__":
	lowerCAmelCase_    =    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''xclip-base-patch32''',
	    type=str,
	    help='''Name of the model.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	lowerCAmelCase_    =    parser.parse_args()
	convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
	print(F'''{solution() = }''')
