



"""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 			_lowercase				(  __snake_case					,__snake_case	)						->   Optional[Any]:
			__lowerCAmelCase      :  Union[str, Any]												=       XCLIPTextConfig()

			# derive patch size from model name
			__lowerCAmelCase      :  Union[str, Any]												=       model_name.find("patch"	)
			__lowerCAmelCase      :  List[str]												=       int(model_name[start_idx + len("patch"	) : start_idx + len("patch"	) + 2]	)
			__lowerCAmelCase      :  List[str]												=       XCLIPVisionConfig(patch_size=__snake_case					,num_frames=__snake_case	)

			if "large" in model_name:
						__lowerCAmelCase      :  Union[str, Any]												=       768
						__lowerCAmelCase      :  List[Any]												=       3_072
						__lowerCAmelCase      :  int												=       12

						__lowerCAmelCase      :  Union[str, Any]												=       1_024
						__lowerCAmelCase      :  Union[str, Any]												=       4_096
						__lowerCAmelCase      :  Dict												=       16
						__lowerCAmelCase      :  Union[str, Any]												=       24
						__lowerCAmelCase      :  Optional[Any]												=       768
						__lowerCAmelCase      :  Optional[Any]												=       3_072

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

			__lowerCAmelCase      :  Dict												=       XCLIPConfig.from_text_vision_configs(__snake_case					,__snake_case	)

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

			return config


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

			return name


def 			_lowercase				(  __snake_case					,__snake_case	)						->   List[str]:
			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase      :  int												=       orig_state_dict.pop(__snake_case	)

						if "attn.in_proj" in key:
									__lowerCAmelCase      :  str												=       key.split("."	)
									if key.startswith("visual"	):
												__lowerCAmelCase      :  Optional[Any]												=       key_split[3]
												__lowerCAmelCase      :  List[Any]												=       config.vision_config.hidden_size
												if "message_attn" in key:
															if "weight" in key:
																		__lowerCAmelCase      :  List[str]												=       val[
																		    :dim, :
																		]
																		__lowerCAmelCase      :  int												=       val[
																		    dim : dim * 2, :
																		]
																		__lowerCAmelCase      :  List[str]												=       val[
																		    -dim:, :
																		]
															else:
																		__lowerCAmelCase      :  Union[str, Any]												=       val[
																		    :dim
																		]
																		__lowerCAmelCase      :  Any												=       val[
																		    dim : dim * 2
																		]
																		__lowerCAmelCase      :  int												=       val[
																		    -dim:
																		]
												else:
															if "weight" in key:
																		__lowerCAmelCase      :  Optional[Any]												=       val[
																		    :dim, :
																		]
																		__lowerCAmelCase      :  Optional[Any]												=       val[
																		    dim : dim * 2, :
																		]
																		__lowerCAmelCase      :  List[str]												=       val[
																		    -dim:, :
																		]
															else:
																		__lowerCAmelCase      :  Optional[int]												=       val[:dim]
																		__lowerCAmelCase      :  List[str]												=       val[
																		    dim : dim * 2
																		]
																		__lowerCAmelCase      :  Optional[int]												=       val[-dim:]
									elif key.startswith("mit"	):
												__lowerCAmelCase      :  Tuple												=       key_split[2]
												__lowerCAmelCase      :  int												=       config.vision_config.mit_hidden_size
												if "weight" in key:
															__lowerCAmelCase      :  int												=       val[:dim, :]
															__lowerCAmelCase      :  int												=       val[dim : dim * 2, :]
															__lowerCAmelCase      :  Dict												=       val[-dim:, :]
												else:
															__lowerCAmelCase      :  Dict												=       val[:dim]
															__lowerCAmelCase      :  List[Any]												=       val[dim : dim * 2]
															__lowerCAmelCase      :  Dict												=       val[-dim:]
									else:
												__lowerCAmelCase      :  Union[str, Any]												=       key_split[2]
												__lowerCAmelCase      :  Dict												=       config.text_config.hidden_size
												if "weight" in key:
															__lowerCAmelCase      :  List[Any]												=       val[:dim, :]
															__lowerCAmelCase      :  List[str]												=       val[
															    dim : dim * 2, :
															]
															__lowerCAmelCase      :  Tuple												=       val[-dim:, :]
												else:
															__lowerCAmelCase      :  str												=       val[:dim]
															__lowerCAmelCase      :  Tuple												=       val[
															    dim : dim * 2
															]
															__lowerCAmelCase      :  List[Any]												=       val[-dim:]
						else:
									__lowerCAmelCase      :  str												=       rename_key(__snake_case	)
									if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
												__lowerCAmelCase      :  Dict												=       val.T
									__lowerCAmelCase      :  Dict												=       val

			return orig_state_dict


def 			_lowercase				(  __snake_case	)						->   List[Any]:
			if num_frames == 8:
						__lowerCAmelCase      :  Any												=       "eating_spaghetti_8_frames.npy"
			elif num_frames == 16:
						__lowerCAmelCase      :  Optional[int]												=       "eating_spaghetti.npy"
			elif num_frames == 32:
						__lowerCAmelCase      :  List[str]												=       "eating_spaghetti_32_frames.npy"
			__lowerCAmelCase      :  List[str]												=       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"					,filename=__snake_case					,repo_type="dataset"					,)
			__lowerCAmelCase      :  List[str]												=       np.load(__snake_case	)
			return list(__snake_case	)


def 			_lowercase				(  __snake_case					,__snake_case=None					,__snake_case=False	)						->   Tuple:
			__lowerCAmelCase      :  Dict												=       {
			    # 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      :  List[Any]												=       model_to_url[model_name]
			__lowerCAmelCase      :  Dict												=       8
			if "16-frames" in model_name:
						__lowerCAmelCase      :  List[Any]												=       16
			elif "shot" in model_name:
						__lowerCAmelCase      :  Tuple												=       32

			__lowerCAmelCase      :  List[str]												=       get_xclip_config(__snake_case					,__snake_case	)
			__lowerCAmelCase      :  int												=       XCLIPModel(__snake_case	)
			model.eval()

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

			__lowerCAmelCase      :  Optional[Any]												=       convert_state_dict(__snake_case					,__snake_case	)

			__lowerCAmelCase      :  List[str]												=       XCLIPModel(__snake_case	)
			__lowerCAmelCase						,	__lowerCAmelCase      :  Dict												=       model.load_state_dict(__snake_case					,strict=__snake_case	)
			assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
			model.eval()

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

			__lowerCAmelCase      :  Union[str, Any]												=       prepare_video(__snake_case	)
			__lowerCAmelCase      :  Any												=       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():
						__lowerCAmelCase      :  Union[str, Any]												=       model(**__snake_case	)

			# Verify outputs
			__lowerCAmelCase      :  List[Any]												=       outputs.logits_per_video
			__lowerCAmelCase      :  List[Any]												=       logits_per_video.softmax(dim=1	)
			print("Probs:"					,__snake_case	)
			# kinetics-400
			if model_name == "xclip-base-patch32":
						__lowerCAmelCase      :  Dict												=       torch.tensor([[0.0019, 0.9951, 0.0030]]	)
			elif model_name == "xclip-base-patch32-16-frames":
						__lowerCAmelCase      :  str												=       torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]	)
			elif model_name == "xclip-base-patch16":
						__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([[0.0083, 0.9681, 0.0236]]	)
			elif model_name == "xclip-base-patch16-16-frames":
						__lowerCAmelCase      :  Union[str, Any]												=       torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]	)
			elif model_name == "xclip-large-patch14":
						__lowerCAmelCase      :  List[str]												=       torch.tensor([[0.0062, 0.9864, 0.0075]]	)
			elif model_name == "xclip-large-patch14-16-frames":
						__lowerCAmelCase      :  Any												=       torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]	)
			# kinetics-600
			elif model_name == "xclip-base-patch16-kinetics-600":
						__lowerCAmelCase      :  Optional[int]												=       torch.tensor([[0.0555, 0.8914, 0.0531]]	)
			elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
						__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]	)
			elif model_name == "xclip-large-patch14-kinetics-600":
						__lowerCAmelCase      :  Any												=       torch.tensor([[0.0036, 0.9920, 0.0045]]	)
			# few shot
			elif model_name == "xclip-base-patch16-hmdb-2-shot":
						__lowerCAmelCase      :  List[Any]												=       torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]	)
			elif model_name == "xclip-base-patch16-hmdb-4-shot":
						__lowerCAmelCase      :  Union[str, Any]												=       torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]	)
			elif model_name == "xclip-base-patch16-hmdb-8-shot":
						__lowerCAmelCase      :  str												=       torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]	)
			elif model_name == "xclip-base-patch16-hmdb-16-shot":
						__lowerCAmelCase      :  Tuple												=       torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]	)
			elif model_name == "xclip-base-patch16-ucf-2-shot":
						__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]	)
			elif model_name == "xclip-base-patch16-ucf-4-shot":
						__lowerCAmelCase      :  Any												=       torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]	)
			elif model_name == "xclip-base-patch16-ucf-8-shot":
						__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([[0.0027, 0.9904, 0.0070]]	)
			elif model_name == "xclip-base-patch16-ucf-16-shot":
						__lowerCAmelCase      :  Dict												=       torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]	)
			# zero shot
			elif model_name == "xclip-base-patch16-zero-shot":
						__lowerCAmelCase      :  int												=       torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]	)
			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__":
	__snake_case							:   str      =    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.'
	)

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




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)