
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_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     XCLIPTextConfig()

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

				if "large" in model_name:
								lowerCamelCase_         =     7_6_8
								lowerCamelCase_         =     3_0_7_2
								lowerCamelCase_         =     1_2

								lowerCamelCase_         =     1_0_2_4
								lowerCamelCase_         =     4_0_9_6
								lowerCamelCase_         =     1_6
								lowerCamelCase_         =     2_4
								lowerCamelCase_         =     7_6_8
								lowerCamelCase_         =     3_0_7_2

				if model_name == "xclip-large-patch14-16-frames":
								lowerCamelCase_         =     3_3_6

				lowerCamelCase_         =     XCLIPConfig.from_text_vision_configs(lowerCamelCase__      ,					lowerCamelCase__       )

				if "large" in model_name:
								lowerCamelCase_         =     7_6_8

				return config





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

				return name





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_         =     orig_state_dict.pop(lowerCamelCase__       )

								if "attn.in_proj" in key:
												lowerCamelCase_         =     key.split("."       )
												if key.startswith("visual"       ):
																lowerCamelCase_         =     key_split[3]
																lowerCamelCase_         =     config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								lowerCamelCase_         =     val[
																								    :dim, :
																								]
																								lowerCamelCase_         =     val[
																								    dim : dim * 2, :
																								]
																								lowerCamelCase_         =     val[
																								    -dim:, :
																								]
																				else:
																								lowerCamelCase_         =     val[
																								    :dim
																								]
																								lowerCamelCase_         =     val[
																								    dim : dim * 2
																								]
																								lowerCamelCase_         =     val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								lowerCamelCase_         =     val[
																								    :dim, :
																								]
																								lowerCamelCase_         =     val[
																								    dim : dim * 2, :
																								]
																								lowerCamelCase_         =     val[
																								    -dim:, :
																								]
																				else:
																								lowerCamelCase_         =     val[:dim]
																								lowerCamelCase_         =     val[
																								    dim : dim * 2
																								]
																								lowerCamelCase_         =     val[-dim:]
												elif key.startswith("mit"       ):
																lowerCamelCase_         =     key_split[2]
																lowerCamelCase_         =     config.vision_config.mit_hidden_size
																if "weight" in key:
																				lowerCamelCase_         =     val[:dim, :]
																				lowerCamelCase_         =     val[dim : dim * 2, :]
																				lowerCamelCase_         =     val[-dim:, :]
																else:
																				lowerCamelCase_         =     val[:dim]
																				lowerCamelCase_         =     val[dim : dim * 2]
																				lowerCamelCase_         =     val[-dim:]
												else:
																lowerCamelCase_         =     key_split[2]
																lowerCamelCase_         =     config.text_config.hidden_size
																if "weight" in key:
																				lowerCamelCase_         =     val[:dim, :]
																				lowerCamelCase_         =     val[
																				    dim : dim * 2, :
																				]
																				lowerCamelCase_         =     val[-dim:, :]
																else:
																				lowerCamelCase_         =     val[:dim]
																				lowerCamelCase_         =     val[
																				    dim : dim * 2
																				]
																				lowerCamelCase_         =     val[-dim:]
								else:
												lowerCamelCase_         =     rename_key(lowerCamelCase__       )
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																lowerCamelCase_         =     val.T
												lowerCamelCase_         =     val

				return orig_state_dict





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if num_frames == 8:
								lowerCamelCase_         =     "eating_spaghetti_8_frames.npy"
				elif num_frames == 1_6:
								lowerCamelCase_         =     "eating_spaghetti.npy"
				elif num_frames == 3_2:
								lowerCamelCase_         =     "eating_spaghetti_32_frames.npy"
				lowerCamelCase_         =     hf_hub_download(
				    repo_id="hf-internal-testing/spaghetti-video"      ,					filename=lowerCamelCase__      ,					repo_type="dataset"      ,					)
				lowerCamelCase_         =     np.load(lowerCamelCase__       )
				return list(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__=None      ,					lowerCamelCase__=False       ):
				lowerCamelCase_         =     {
				    # 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",
				}

				lowerCamelCase_         =     model_to_url[model_name]
				lowerCamelCase_         =     8
				if "16-frames" in model_name:
								lowerCamelCase_         =     1_6
				elif "shot" in model_name:
								lowerCamelCase_         =     3_2

				lowerCamelCase_         =     get_xclip_config(lowerCamelCase__      ,					lowerCamelCase__       )
				lowerCamelCase_         =     XCLIPModel(lowerCamelCase__       )
				model.eval()

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

				lowerCamelCase_         =     convert_state_dict(lowerCamelCase__      ,					lowerCamelCase__       )

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

				lowerCamelCase_         =     3_3_6 if model_name == "xclip-large-patch14-16-frames" else 2_2_4
				lowerCamelCase_         =     VideoMAEImageProcessor(size=lowerCamelCase__       )
				lowerCamelCase_         =     CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"       )
				lowerCamelCase_         =     CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"       )
				lowerCamelCase_         =     XCLIPProcessor(image_processor=lowerCamelCase__      ,					tokenizer=lowerCamelCase__       )

				lowerCamelCase_         =     prepare_video(lowerCamelCase__       )
				lowerCamelCase_         =     processor(
				    text=["playing sports", "eating spaghetti", "go shopping"]      ,					videos=lowerCamelCase__      ,					return_tensors="pt"      ,					padding=lowerCamelCase__       )

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

				with torch.no_grad():
								lowerCamelCase_         =     model(**lowerCamelCase__       )

				# Verify outputs
				lowerCamelCase_         =     outputs.logits_per_video
				lowerCamelCase_         =     logits_per_video.softmax(dim=1       )
				print("Probs:"      ,					lowerCamelCase__       )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								lowerCamelCase_         =     torch.tensor([[0.00_19, 0.99_51, 0.00_30]]       )
				elif model_name == "xclip-base-patch32-16-frames":
								lowerCamelCase_         =     torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]       )
				elif model_name == "xclip-base-patch16":
								lowerCamelCase_         =     torch.tensor([[0.00_83, 0.96_81, 0.02_36]]       )
				elif model_name == "xclip-base-patch16-16-frames":
								lowerCamelCase_         =     torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]       )
				elif model_name == "xclip-large-patch14":
								lowerCamelCase_         =     torch.tensor([[0.00_62, 0.98_64, 0.00_75]]       )
				elif model_name == "xclip-large-patch14-16-frames":
								lowerCamelCase_         =     torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]       )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								lowerCamelCase_         =     torch.tensor([[0.05_55, 0.89_14, 0.05_31]]       )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								lowerCamelCase_         =     torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]       )
				elif model_name == "xclip-large-patch14-kinetics-600":
								lowerCamelCase_         =     torch.tensor([[0.00_36, 0.99_20, 0.00_45]]       )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								lowerCamelCase_         =     torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								lowerCamelCase_         =     torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								lowerCamelCase_         =     torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]       )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								lowerCamelCase_         =     torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								lowerCamelCase_         =     torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								lowerCamelCase_         =     torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]       )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								lowerCamelCase_         =     torch.tensor([[0.00_27, 0.99_04, 0.00_70]]       )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								lowerCamelCase_         =     torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]       )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								lowerCamelCase_         =     torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]       )
				else:
								raise ValueError(F'Model name {model_name} not supported'       )
				assert torch.allclose(lowerCamelCase__      ,					lowerCamelCase__      ,					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(lowerCamelCase__       )

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




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'MCTCTFeatureExtractor'
	lowerCAmelCase__   =					'AutoTokenizer'



	def __init__(       self		,    lowercase		,    lowercase					)							->			str:
					super().__init__(lowercase		,    lowercase					)
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False



	def __call__(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase		,    **lowercase					)

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."					)
									lowerCamelCase_         =     kwargs.pop("raw_speech"					)
					else:
									lowerCamelCase_         =     kwargs.pop("audio"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("sampling_rate"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("text"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."					)

					if audio is not None:
									lowerCamelCase_         =     self.feature_extractor(lowercase		,    *lowercase		,    sampling_rate=lowercase		,    **lowercase					)
					if text is not None:
									lowerCamelCase_         =     self.tokenizer(lowercase		,    **lowercase					)

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_         =     encodings["input_ids"]
									return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					return self.tokenizer.batch_decode(*lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase		,    **lowercase					)

					lowerCamelCase_         =     kwargs.pop("input_features"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("labels"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if input_features is not None:
									lowerCamelCase_         =     self.feature_extractor.pad(lowercase		,    *lowercase		,    **lowercase					)
					if labels is not None:
									lowerCamelCase_         =     self.tokenizer.pad(lowercase		,    **lowercase					)

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_         =     labels["input_ids"]
									return input_features



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					return self.tokenizer.decode(*lowercase		,    **lowercase					)



	@contextmanager
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call."					)
					lowerCamelCase_         =     True
					lowerCamelCase_         =     self.tokenizer
					yield
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False


