
"""simple docstring"""


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    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  str  ):
	A     = XCLIPTextConfig()

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

	if "large" in model_name:
		A     = 768
		A     = 3072
		A     = 12

		A     = 1024
		A     = 4096
		A     = 16
		A     = 24
		A     = 768
		A     = 3072

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

	A     = XCLIPConfig.from_text_vision_configs(snake_case__						,   snake_case__  )

	if "large" in model_name:
		A     = 768

	return config






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

	return name






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  str  ):
	for key in orig_state_dict.copy().keys():
		A     = orig_state_dict.pop(snake_case__  )

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

	return orig_state_dict






def    _snake_case					(		snake_case__ :  Union[str, Any]  ):
	if num_frames == 8:
		A     = 'eating_spaghetti_8_frames.npy'
	elif num_frames == 16:
		A     = 'eating_spaghetti.npy'
	elif num_frames == 32:
		A     = 'eating_spaghetti_32_frames.npy'
	A     = hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'						,   filename=snake_case__						,   repo_type='dataset'						,   )
	A     = np.load(snake_case__  )
	return list(snake_case__  )






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  List[str]=None						,   snake_case__ :  Tuple=False  ):
	A     = {
	    # 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',
	}

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

	A     = get_xclip_config(snake_case__						,   snake_case__  )
	A     = XCLIPModel(snake_case__  )
	model.eval()

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

	A     = convert_state_dict(snake_case__						,   snake_case__  )

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

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

	A     = prepare_video(snake_case__  )
	A     = 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():
		A     = model(**snake_case__  )

	# Verify outputs
	A     = outputs.logits_per_video
	A     = logits_per_video.softmax(dim=1  )
	print('Probs:'						,   snake_case__  )
	# kinetics-400
	if model_name == "xclip-base-patch32":
		A     = torch.tensor([[0.0019, 0.9951, 0.0030]]  )
	elif model_name == "xclip-base-patch32-16-frames":
		A     = torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]  )
	elif model_name == "xclip-base-patch16":
		A     = torch.tensor([[0.0083, 0.9681, 0.0236]]  )
	elif model_name == "xclip-base-patch16-16-frames":
		A     = torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]  )
	elif model_name == "xclip-large-patch14":
		A     = torch.tensor([[0.0062, 0.9864, 0.0075]]  )
	elif model_name == "xclip-large-patch14-16-frames":
		A     = torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]  )
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		A     = torch.tensor([[0.0555, 0.8914, 0.0531]]  )
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		A     = torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]  )
	elif model_name == "xclip-large-patch14-kinetics-600":
		A     = torch.tensor([[0.0036, 0.9920, 0.0045]]  )
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		A     = torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		A     = torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		A     = torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]  )
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		A     = torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]  )
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		A     = torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]  )
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		A     = torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]  )
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		A     = torch.tensor([[0.0027, 0.9904, 0.0070]]  )
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		A     = torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]  )
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		A     = torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-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__":
		_lowercase				 =					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.'''
		)

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

"""simple docstring"""


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset