





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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    str:
		__lowercase											=     XCLIPTextConfig()

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

		if "large" in model_name:
				__lowercase											=     768
				__lowercase											=     3_072
				__lowercase											=     12

				__lowercase											=     1_024
				__lowercase											=     4_096
				__lowercase											=     16
				__lowercase											=     24
				__lowercase											=     768
				__lowercase											=     3_072

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

		__lowercase											=     XCLIPConfig.from_text_vision_configs(snake_case						,   snake_case							)

		if "large" in model_name:
				__lowercase											=     768

		return config



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

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

				if "attn.in_proj" in key:
						__lowercase											=     key.split('.'							)
						if key.startswith('visual'							):
								__lowercase											=     key_split[3]
								__lowercase											=     config.vision_config.hidden_size
								if "message_attn" in key:
										if "weight" in key:
												__lowercase											=     val[
												    :dim, :
												]
												__lowercase											=     val[
												    dim : dim * 2, :
												]
												__lowercase											=     val[
												    -dim:, :
												]
										else:
												__lowercase											=     val[
												    :dim
												]
												__lowercase											=     val[
												    dim : dim * 2
												]
												__lowercase											=     val[
												    -dim:
												]
								else:
										if "weight" in key:
												__lowercase											=     val[
												    :dim, :
												]
												__lowercase											=     val[
												    dim : dim * 2, :
												]
												__lowercase											=     val[
												    -dim:, :
												]
										else:
												__lowercase											=     val[:dim]
												__lowercase											=     val[
												    dim : dim * 2
												]
												__lowercase											=     val[-dim:]
						elif key.startswith('mit'							):
								__lowercase											=     key_split[2]
								__lowercase											=     config.vision_config.mit_hidden_size
								if "weight" in key:
										__lowercase											=     val[:dim, :]
										__lowercase											=     val[dim : dim * 2, :]
										__lowercase											=     val[-dim:, :]
								else:
										__lowercase											=     val[:dim]
										__lowercase											=     val[dim : dim * 2]
										__lowercase											=     val[-dim:]
						else:
								__lowercase											=     key_split[2]
								__lowercase											=     config.text_config.hidden_size
								if "weight" in key:
										__lowercase											=     val[:dim, :]
										__lowercase											=     val[
										    dim : dim * 2, :
										]
										__lowercase											=     val[-dim:, :]
								else:
										__lowercase											=     val[:dim]
										__lowercase											=     val[
										    dim : dim * 2
										]
										__lowercase											=     val[-dim:]
				else:
						__lowercase											=     rename_key(snake_case							)
						if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
								__lowercase											=     val.T
						__lowercase											=     val

		return orig_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    str:
		if num_frames == 8:
				__lowercase											=     'eating_spaghetti_8_frames.npy'
		elif num_frames == 16:
				__lowercase											=     'eating_spaghetti.npy'
		elif num_frames == 32:
				__lowercase											=     'eating_spaghetti_32_frames.npy'
		__lowercase											=     hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'						,   filename=snake_case						,   repo_type='dataset'						,   )
		__lowercase											=     np.load(snake_case							)
		return list(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Dict:
		__lowercase											=     {
		    # 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',
		}

		__lowercase											=     model_to_url[model_name]
		__lowercase											=     8
		if "16-frames" in model_name:
				__lowercase											=     16
		elif "shot" in model_name:
				__lowercase											=     32

		__lowercase											=     get_xclip_config(snake_case						,   snake_case							)
		__lowercase											=     XCLIPModel(snake_case							)
		model.eval()

		if "drive" in checkpoint_url:
				__lowercase											=     'pytorch_model.bin'
				gdown.cached_download(snake_case						,   snake_case						,   quiet=snake_case							)
				__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)['model']
		else:
				__lowercase											=     torch.hub.load_state_dict_from_url(snake_case							)['model']

		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)

		__lowercase											=     XCLIPModel(snake_case							)
		__lowercase						,      __lowercase											=     model.load_state_dict(snake_case						,   strict=snake_case							)
		assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
		model.eval()

		__lowercase											=     336 if model_name == 'xclip-large-patch14-16-frames' else 224
		__lowercase											=     VideoMAEImageProcessor(size=snake_case							)
		__lowercase											=     CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
		__lowercase											=     CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'							)
		__lowercase											=     XCLIPProcessor(image_processor=snake_case						,   tokenizer=snake_case							)

		__lowercase											=     prepare_video(snake_case							)
		__lowercase											=     processor(
		    text=['playing sports', 'eating spaghetti', 'go shopping']						,   videos=snake_case						,   return_tensors='pt'						,   padding=snake_case							)

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

		with torch.no_grad():
				__lowercase											=     model(**snake_case							)

		# Verify outputs
		__lowercase											=     outputs.logits_per_video
		__lowercase											=     logits_per_video.softmax(dim=1							)
		print('Probs:'						,   snake_case							)
		# kinetics-400
		if model_name == "xclip-base-patch32":
				__lowercase											=     torch.tensor([[0.0019, 0.9951, 0.0030]]							)
		elif model_name == "xclip-base-patch32-16-frames":
				__lowercase											=     torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]							)
		elif model_name == "xclip-base-patch16":
				__lowercase											=     torch.tensor([[0.0083, 0.9681, 0.0236]]							)
		elif model_name == "xclip-base-patch16-16-frames":
				__lowercase											=     torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]							)
		elif model_name == "xclip-large-patch14":
				__lowercase											=     torch.tensor([[0.0062, 0.9864, 0.0075]]							)
		elif model_name == "xclip-large-patch14-16-frames":
				__lowercase											=     torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]							)
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				__lowercase											=     torch.tensor([[0.0555, 0.8914, 0.0531]]							)
		elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
				__lowercase											=     torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]							)
		elif model_name == "xclip-large-patch14-kinetics-600":
				__lowercase											=     torch.tensor([[0.0036, 0.9920, 0.0045]]							)
		# few shot
		elif model_name == "xclip-base-patch16-hmdb-2-shot":
				__lowercase											=     torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]							)
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				__lowercase											=     torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]							)
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				__lowercase											=     torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]							)
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				__lowercase											=     torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]							)
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				__lowercase											=     torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				__lowercase											=     torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				__lowercase											=     torch.tensor([[0.0027, 0.9904, 0.0070]]							)
		elif model_name == "xclip-base-patch16-ucf-16-shot":
				__lowercase											=     torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]							)
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				__lowercase											=     torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]							)
		else:
				raise ValueError(F"Model name {model_name} not supported"							)
		assert torch.allclose(snake_case						,   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(snake_case							)

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      int			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











from math import factorial



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:

		# If either of the conditions are true, the function is being asked
		# to calculate a factorial of a negative number, which is not possible
		if n < k or k < 0:
				raise ValueError('Please enter positive integers for n and k where n >= k'							)
		return factorial(snake_case							) // (factorial(snake_case							) * factorial(n - k							))


if __name__ == "__main__":
		print(
		    '''The number of five-card hands possible from a standard''',
		    F"""fifty-two card deck is: {combinations(52, 5)}\n""",
		)

		print(
		    '''If a class of 40 students must be arranged into groups of''',
		    F"""4 for group projects, there are {combinations(40, 4)} ways""",
		    '''to arrange them.\n''',
		)

		print(
		    '''If 10 teams are competing in a Formula One race, there''',
		    F"""are {combinations(10, 3)} ways that first, second and""",
		    '''third place can be awarded.''',
		)




