



"""simple docstring"""
from jiwer import compute_measures

import datasets


__a      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__a      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__a      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION				)
class 				lowerCamelCase  (			datasets.Metric				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Optional[Any]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""						,     id="""sequence"""						),
											            """references""": datasets.Value("""string"""						,     id="""sequence"""						),
											        }						)						,     codebase_urls=["""https://github.com/jitsi/jiwer/"""]						,     reference_urls=[
											        """https://en.wikipedia.org/wiki/Word_error_rate""",
											    ]						,     )






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Optional[Any]=None						,     snake_case:							Dict=None						,     snake_case:							Any=False						)   ->	Optional[int]:
											if concatenate_texts:
																	return compute_measures(snake_case						,     snake_case						)["wer"]
											else:
																	snake_case_ :List[str]						=			0
																	snake_case_ :Dict						=			0
																	for prediction, reference in zip(snake_case						,     snake_case						):
																							snake_case_ :List[str]						=			compute_measures(snake_case						,     snake_case						)
																							incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																							total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																	return incorrect / total





"""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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			XCLIPTextConfig()

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

						if "large" in model_name:
												snake_case_ :Optional[Any]						=			768
												snake_case_ :Union[str, Any]						=			3072
												snake_case_ :Any						=			12

												snake_case_ :Any						=			1024
												snake_case_ :str						=			4096
												snake_case_ :Union[str, Any]						=			16
												snake_case_ :Union[str, Any]						=			24
												snake_case_ :Tuple						=			768
												snake_case_ :Any						=			3072

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

						snake_case_ :Any						=			XCLIPConfig.from_text_vision_configs(_lowercase,					_lowercase	)

						if "large" in model_name:
												snake_case_ :List[Any]						=			768

						return config
def    A_  (			_lowercase	):







						'''simple docstring'''


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

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

						return orig_state_dict
def    A_  (			_lowercase	):







						'''simple docstring'''


						if num_frames == 8:
												snake_case_ :str						=			"""eating_spaghetti_8_frames.npy"""
						elif num_frames == 16:
												snake_case_ :int						=			"""eating_spaghetti.npy"""
						elif num_frames == 32:
												snake_case_ :List[str]						=			"""eating_spaghetti_32_frames.npy"""
						snake_case_ :int						=			hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video""",					filename=_lowercase,					repo_type="""dataset""",					)
						snake_case_ :Union[str, Any]						=			np.load(_lowercase	)
						return list(_lowercase	)
def    A_  (			_lowercase,					_lowercase=None,					_lowercase=False	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{
						    # 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_ :Optional[int]						=			model_to_url[model_name]
						snake_case_ :int						=			8
						if "16-frames" in model_name:
												snake_case_ :List[Any]						=			16
						elif "shot" in model_name:
												snake_case_ :Dict						=			32

						snake_case_ :Optional[int]						=			get_xclip_config(_lowercase,					_lowercase	)
						snake_case_ :Optional[Any]						=			XCLIPModel(_lowercase	)
						model.eval()

						if "drive" in checkpoint_url:
												snake_case_ :List[str]						=			"""pytorch_model.bin"""
												gdown.cached_download(_lowercase,					_lowercase,					quiet=_lowercase	)
												snake_case_ :List[Any]						=			torch.load(_lowercase,					map_location="""cpu"""	)["""model"""]
						else:
												snake_case_ :Tuple						=			torch.hub.load_state_dict_from_url(_lowercase	)["""model"""]

						snake_case_ :Union[str, Any]						=			convert_state_dict(_lowercase,					_lowercase	)

						snake_case_ :str						=			XCLIPModel(_lowercase	)
						snake_case_,       snake_case_ :Optional[int]						=			model.load_state_dict(_lowercase,					strict=_lowercase	)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

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

						snake_case_ :Optional[int]						=			prepare_video(_lowercase	)
						snake_case_ :Optional[Any]						=			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_ :List[Any]						=			model(**_lowercase	)

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