

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 a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			XCLIPTextConfig()

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

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

		snake_case_   		=			1024
		snake_case_   		=			4096
		snake_case_   		=			16
		snake_case_   		=			24
		snake_case_   		=			768
		snake_case_   		=			3072

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

	snake_case_   		=			XCLIPConfig.from_text_vision_configs(lowercase__      ,			lowercase__						)

	if "large" in model_name:
		snake_case_   		=			768

	return config





def a(lowercase__						):





	'''simple docstring'''
	# text encoder
	if name == "token_embedding.weight":
		snake_case_   		=			name.replace('token_embedding.weight'      ,			'text_model.embeddings.token_embedding.weight'						)
	if name == "positional_embedding":
		snake_case_   		=			name.replace('positional_embedding'      ,			'text_model.embeddings.position_embedding.weight'						)
	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 name.startswith('transformer.resblocks'						):
		snake_case_   		=			name.replace('transformer.resblocks'      ,			'text_model.encoder.layers'						)
	if "attn.out_proj" in name and "message" not in name:
		snake_case_   		=			name.replace('attn.out_proj'      ,			'self_attn.out_proj'						)
	if "ln_final" in name:
		snake_case_   		=			name.replace('ln_final'      ,			'text_model.final_layer_norm'						)
	# visual encoder
	if name == "visual.class_embedding":
		snake_case_   		=			name.replace('visual.class_embedding'      ,			'vision_model.embeddings.class_embedding'						)
	if name == "visual.positional_embedding":
		snake_case_   		=			name.replace('visual.positional_embedding'      ,			'vision_model.embeddings.position_embedding.weight'						)
	if name.startswith('visual.transformer.resblocks'						):
		snake_case_   		=			name.replace('visual.transformer.resblocks'      ,			'vision_model.encoder.layers'						)
	if "visual.conv1" in name:
		snake_case_   		=			name.replace('visual.conv1'      ,			'vision_model.embeddings.patch_embedding'						)
	if "visual.ln_pre" in name:
		snake_case_   		=			name.replace('visual.ln_pre'      ,			'vision_model.pre_layernorm'						)
	if "visual.ln_post" in name:
		snake_case_   		=			name.replace('visual.ln_post'      ,			'vision_model.post_layernorm'						)
	if "visual.proj" in name:
		snake_case_   		=			name.replace('visual.proj'      ,			'visual_projection.weight'						)
	if "text_projection" in name:
		snake_case_   		=			name.replace('text_projection'      ,			'text_projection.weight'						)
	# things on top
	if "prompts_visual_proj" in name:
		snake_case_   		=			name.replace('prompts_visual_proj'      ,			'prompts_visual_projection'						)
	if "prompts_visual_ln" in name:
		snake_case_   		=			name.replace('prompts_visual_ln'      ,			'prompts_visual_layernorm'						)
	# mit
	if name == "mit.positional_embedding":
		snake_case_   		=			name.replace('positional'      ,			'position'						)
	if name.startswith('mit.resblocks'						):
		snake_case_   		=			name.replace('mit.resblocks'      ,			'mit.encoder.layers'						)
	# prompts generator
	if name.startswith('prompts_generator.norm'						):
		snake_case_   		=			name.replace('prompts_generator.norm'      ,			'prompts_generator.layernorm'						)

	return name





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	for key in orig_state_dict.copy().keys():
		snake_case_   		=			orig_state_dict.pop(lowercase__						)

		if "attn.in_proj" in key:
			snake_case_   		=			key.split('.'						)
			if key.startswith('visual'						):
				snake_case_   		=			key_split[3]
				snake_case_   		=			config.vision_config.hidden_size
				if "message_attn" in key:
					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:
					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 key.startswith('mit'						):
				snake_case_   		=			key_split[2]
				snake_case_   		=			config.vision_config.mit_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_   		=			key_split[2]
				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(lowercase__						)
			if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
				snake_case_   		=			val.T
			snake_case_   		=			val

	return orig_state_dict





def a(lowercase__						):





	'''simple docstring'''
	if num_frames == 8:
		snake_case_   		=			'eating_spaghetti_8_frames.npy'
	elif num_frames == 16:
		snake_case_   		=			'eating_spaghetti.npy'
	elif num_frames == 32:
		snake_case_   		=			'eating_spaghetti_32_frames.npy'
	snake_case_   		=			hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'      ,			filename=lowercase__      ,			repo_type='dataset'      ,			)
	snake_case_   		=			np.load(lowercase__						)
	return list(lowercase__						)





def a(lowercase__      ,			lowercase__=None      ,			lowercase__=False						):





	'''simple docstring'''
	snake_case_   		=			{
	    # 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_   		=			model_to_url[model_name]
	snake_case_   		=			8
	if "16-frames" in model_name:
		snake_case_   		=			16
	elif "shot" in model_name:
		snake_case_   		=			32

	snake_case_   		=			get_xclip_config(lowercase__      ,			lowercase__						)
	snake_case_   		=			XCLIPModel(lowercase__						)
	model.eval()

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

	snake_case_   		=			convert_state_dict(lowercase__      ,			lowercase__						)

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

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

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

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

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

	# Verify outputs
	snake_case_   		=			outputs.logits_per_video
	snake_case_   		=			logits_per_video.softmax(dim=1						)
	print('Probs:'      ,			lowercase__						)
	# kinetics-400
	if model_name == "xclip-base-patch32":
		snake_case_   		=			torch.tensor([[0.0019, 0.9951, 0.0030]]						)
	elif model_name == "xclip-base-patch32-16-frames":
		snake_case_   		=			torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]						)
	elif model_name == "xclip-base-patch16":
		snake_case_   		=			torch.tensor([[0.0083, 0.9681, 0.0236]]						)
	elif model_name == "xclip-base-patch16-16-frames":
		snake_case_   		=			torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]						)
	elif model_name == "xclip-large-patch14":
		snake_case_   		=			torch.tensor([[0.0062, 0.9864, 0.0075]]						)
	elif model_name == "xclip-large-patch14-16-frames":
		snake_case_   		=			torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]						)
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		snake_case_   		=			torch.tensor([[0.0555, 0.8914, 0.0531]]						)
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		snake_case_   		=			torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]						)
	elif model_name == "xclip-large-patch14-kinetics-600":
		snake_case_   		=			torch.tensor([[0.0036, 0.9920, 0.0045]]						)
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		snake_case_   		=			torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]						)
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		snake_case_   		=			torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]						)
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		snake_case_   		=			torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]						)
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		snake_case_   		=			torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]						)
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		snake_case_   		=			torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]						)
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		snake_case_   		=			torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]						)
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		snake_case_   		=			torch.tensor([[0.0027, 0.9904, 0.0070]]						)
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		snake_case_   		=			torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]						)
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		snake_case_   		=			torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]						)
	else:
		raise ValueError(f"""Model name {model_name} not supported"""						)
	assert torch.allclose(lowercase__      ,			lowercase__      ,			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(lowercase__						)

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


if __name__ == "__main__":
		A									=  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.'
		)

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



from ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)
