




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCamelCase__					    =    """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

UpperCamelCase__					    =    """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

UpperCamelCase__					    =    """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,       _KWARGS_DESCRIPTION   )
class     a__					(      datasets.Metric   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return datasets.MetricInfo(
											    description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
											        {
											            "predictions": datasets.Sequence(datasets.Value("string"	,    id="token"    )	,    id="sequence"    ),
											            "references": datasets.Sequence(
											                datasets.Sequence(datasets.Value("string"	,    id="token"    )	,    id="sequence"    )	,    id="references"    ),
											        }    )	,    codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]	,    reference_urls=[
											        "https://en.wikipedia.org/wiki/BLEU",
											        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
											    ]	,    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A=4	,    _A=False    ):




											"""simple docstring"""



											__lowerCAmelCase            = compute_bleu(
											    reference_corpus=_A	,    translation_corpus=_A	,    max_order=_A	,    smooth=_A    )
											((__lowerCAmelCase)     ,							(__lowerCAmelCase)     ,							(__lowerCAmelCase)     ,							(__lowerCAmelCase)     ,							(__lowerCAmelCase)     ,							(__lowerCAmelCase))            = score
											return {
											    "bleu": bleu,
											    "precisions": precisions,
											    "brevity_penalty": bp,
											    "length_ratio": ratio,
											    "translation_length": translation_length,
											    "reference_length": reference_length,
											}










import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple  ):
					__lowerCAmelCase            = VideoMAEConfig()

					set_architecture_configs(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					if "finetuned" not in model_name:
										__lowerCAmelCase            = False

					if "finetuned" in model_name:
										__lowerCAmelCase            = "huggingface/label-files"
										if "kinetics" in model_name:
															__lowerCAmelCase            = 4_00
															__lowerCAmelCase            = "kinetics400-id2label.json"
										elif "ssv2" in model_name:
															__lowerCAmelCase            = 1_74
															__lowerCAmelCase            = "something-something-v2-id2label.json"
										else:
															raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."  )
										__lowerCAmelCase            = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   repo_type="dataset"  )   ,   "r"  )  )
										__lowerCAmelCase            = {int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
										__lowerCAmelCase            = idalabel
										__lowerCAmelCase            = {v: k for k, v in idalabel.items()}

					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       List[str]  ):
					if "small" in model_name:
										__lowerCAmelCase            = 3_84
										__lowerCAmelCase            = 15_36
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 16
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 3
										__lowerCAmelCase            = 1_92
										__lowerCAmelCase            = 7_68
					elif "large" in model_name:
										__lowerCAmelCase            = 10_24
										__lowerCAmelCase            = 40_96
										__lowerCAmelCase            = 24
										__lowerCAmelCase            = 16
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 8
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = 20_48
					elif "huge" in model_name:
										__lowerCAmelCase            = 12_80
										__lowerCAmelCase            = 51_20
										__lowerCAmelCase            = 32
										__lowerCAmelCase            = 16
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 8
										__lowerCAmelCase            = 6_40
										__lowerCAmelCase            = 25_60
					elif "base" not in model_name:
										raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ):
					if "encoder." in name:
										__lowerCAmelCase            = name.replace("encoder."   ,   ""  )
					if "cls_token" in name:
										__lowerCAmelCase            = name.replace("cls_token"   ,   "videomae.embeddings.cls_token"  )
					if "decoder_pos_embed" in name:
										__lowerCAmelCase            = name.replace("decoder_pos_embed"   ,   "decoder.decoder_pos_embed"  )
					if "pos_embed" in name and "decoder" not in name:
										__lowerCAmelCase            = name.replace("pos_embed"   ,   "videomae.embeddings.position_embeddings"  )
					if "patch_embed.proj" in name:
										__lowerCAmelCase            = name.replace("patch_embed.proj"   ,   "videomae.embeddings.patch_embeddings.projection"  )
					if "patch_embed.norm" in name:
										__lowerCAmelCase            = name.replace("patch_embed.norm"   ,   "videomae.embeddings.norm"  )
					if "decoder.blocks" in name:
										__lowerCAmelCase            = name.replace("decoder.blocks"   ,   "decoder.decoder_layers"  )
					if "blocks" in name:
										__lowerCAmelCase            = name.replace("blocks"   ,   "videomae.encoder.layer"  )
					if "attn.proj" in name:
										__lowerCAmelCase            = name.replace("attn.proj"   ,   "attention.output.dense"  )
					if "attn" in name and "bias" not in name:
										__lowerCAmelCase            = name.replace("attn"   ,   "attention.self"  )
					if "attn" in name:
										__lowerCAmelCase            = name.replace("attn"   ,   "attention.attention"  )
					if "norm1" in name:
										__lowerCAmelCase            = name.replace("norm1"   ,   "layernorm_before"  )
					if "norm2" in name:
										__lowerCAmelCase            = name.replace("norm2"   ,   "layernorm_after"  )
					if "mlp.fc1" in name:
										__lowerCAmelCase            = name.replace("mlp.fc1"   ,   "intermediate.dense"  )
					if "mlp.fc2" in name:
										__lowerCAmelCase            = name.replace("mlp.fc2"   ,   "output.dense"  )
					if "decoder_embed" in name:
										__lowerCAmelCase            = name.replace("decoder_embed"   ,   "decoder.decoder_embed"  )
					if "decoder_norm" in name:
										__lowerCAmelCase            = name.replace("decoder_norm"   ,   "decoder.decoder_norm"  )
					if "decoder_pred" in name:
										__lowerCAmelCase            = name.replace("decoder_pred"   ,   "decoder.decoder_pred"  )
					if "norm.weight" in name and "decoder" not in name and "fc" not in name:
										__lowerCAmelCase            = name.replace("norm.weight"   ,   "videomae.layernorm.weight"  )
					if "norm.bias" in name and "decoder" not in name and "fc" not in name:
										__lowerCAmelCase            = name.replace("norm.bias"   ,   "videomae.layernorm.bias"  )
					if "head" in name and "decoder" not in name:
										__lowerCAmelCase            = name.replace("head"   ,   "classifier"  )

					return name







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Tuple  ):
					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase            = orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

										if key.startswith("encoder."  ):
															__lowerCAmelCase            = key.replace("encoder."   ,   ""  )

										if "qkv" in key:
															__lowerCAmelCase            = key.split("."  )
															if key.startswith("decoder.blocks"  ):
																				__lowerCAmelCase            = config.decoder_hidden_size
																				__lowerCAmelCase            = int(key_split[2]  )
																				__lowerCAmelCase            = "decoder.decoder_layers."
																				if "weight" in key:
																									__lowerCAmelCase            = val[:dim, :]
																									__lowerCAmelCase            = val[dim : dim * 2, :]
																									__lowerCAmelCase            = val[-dim:, :]
															else:
																				__lowerCAmelCase            = config.hidden_size
																				__lowerCAmelCase            = int(key_split[1]  )
																				__lowerCAmelCase            = "videomae.encoder.layer."
																				if "weight" in key:
																									__lowerCAmelCase            = val[:dim, :]
																									__lowerCAmelCase            = val[dim : dim * 2, :]
																									__lowerCAmelCase            = val[-dim:, :]
										else:
															__lowerCAmelCase            = val

					return orig_state_dict







def 			_a   (		):
					__lowerCAmelCase            = hf_hub_download(
					    repo_id="hf-internal-testing/spaghetti-video"   ,   filename="eating_spaghetti.npy"   ,   repo_type="dataset"  )
					__lowerCAmelCase            = np.load(SCREAMING_SNAKE_CASE_  )
					return list(SCREAMING_SNAKE_CASE_  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       List[str]  ):
					__lowerCAmelCase            = get_videomae_config(SCREAMING_SNAKE_CASE_  )

					if "finetuned" in model_name:
										__lowerCAmelCase            = VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE_  )
					else:
										__lowerCAmelCase            = VideoMAEForPreTraining(SCREAMING_SNAKE_CASE_  )

					# download original checkpoint, hosted on Google Drive
					__lowerCAmelCase            = "pytorch_model.bin"
					gdown.cached_download(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   quiet=SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )
					if "model" in files:
										__lowerCAmelCase            = files["model"]
					else:
										__lowerCAmelCase            = files["module"]
					__lowerCAmelCase            = convert_state_dict(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					model.load_state_dict(SCREAMING_SNAKE_CASE_  )
					model.eval()

					# verify model on basic input
					__lowerCAmelCase            = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,   image_std=[0.5, 0.5, 0.5]  )
					__lowerCAmelCase            = prepare_video()
					__lowerCAmelCase            = image_processor(SCREAMING_SNAKE_CASE_   ,   return_tensors="pt"  )

					if "finetuned" not in model_name:
										__lowerCAmelCase            = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"   ,   filename="bool_masked_pos.pt"  )
										__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = model(**SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = outputs.logits

					__lowerCAmelCase            = [
					    "videomae-small-finetuned-kinetics",
					    "videomae-small-finetuned-ssv2",
					    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
					    "videomae-base-short",
					    "videomae-base-short-finetuned-kinetics",
					    "videomae-base",
					    "videomae-base-finetuned-kinetics",
					    "videomae-large",
					    "videomae-large-finetuned-kinetics",
					    "videomae-huge-finetuned-kinetics",
					    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
					    "videomae-base-short-ssv2",
					    "videomae-base-short-finetuned-ssv2",
					    "videomae-base-ssv2",
					    "videomae-base-finetuned-ssv2",
					]

					# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
					if model_name == "videomae-small-finetuned-kinetics":
										__lowerCAmelCase            = torch.Size([1, 4_00]  )
										__lowerCAmelCase            = torch.tensor([-0.92_91, -0.40_61, -0.93_07]  )
					elif model_name == "videomae-small-finetuned-ssv2":
										__lowerCAmelCase            = torch.Size([1, 1_74]  )
										__lowerCAmelCase            = torch.tensor([0.26_71, -0.46_89, -0.82_35]  )
					elif model_name == "videomae-base":
										__lowerCAmelCase            = torch.Size([1, 14_08, 15_36]  )
										__lowerCAmelCase            = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]  )
					elif model_name == "videomae-base-short":
										__lowerCAmelCase            = torch.Size([1, 14_08, 15_36]  )
										__lowerCAmelCase            = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]  )
										# we verified the loss both for normalized and unnormalized targets for this one
										__lowerCAmelCase            = torch.tensor([0.51_42]  ) if config.norm_pix_loss else torch.tensor([0.64_69]  )
					elif model_name == "videomae-large":
										__lowerCAmelCase            = torch.Size([1, 14_08, 15_36]  )
										__lowerCAmelCase            = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]  )
					elif model_name == "videomae-large-finetuned-kinetics":
										__lowerCAmelCase            = torch.Size([1, 4_00]  )
										__lowerCAmelCase            = torch.tensor([0.07_71, 0.00_11, -0.36_25]  )
					elif model_name == "videomae-huge-finetuned-kinetics":
										__lowerCAmelCase            = torch.Size([1, 4_00]  )
										__lowerCAmelCase            = torch.tensor([0.24_33, 0.16_32, -0.48_94]  )
					elif model_name == "videomae-base-short-finetuned-kinetics":
										__lowerCAmelCase            = torch.Size([1, 4_00]  )
										__lowerCAmelCase            = torch.tensor([0.65_88, 0.09_90, -0.24_93]  )
					elif model_name == "videomae-base-finetuned-kinetics":
										__lowerCAmelCase            = torch.Size([1, 4_00]  )
										__lowerCAmelCase            = torch.tensor([0.36_69, -0.06_88, -0.24_21]  )
					elif model_name == "videomae-base-short-ssv2":
										__lowerCAmelCase            = torch.Size([1, 14_08, 15_36]  )
										__lowerCAmelCase            = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]  )
					elif model_name == "videomae-base-short-finetuned-ssv2":
										__lowerCAmelCase            = torch.Size([1, 1_74]  )
										__lowerCAmelCase            = torch.tensor([-0.05_37, -0.15_39, -0.32_66]  )
					elif model_name == "videomae-base-ssv2":
										__lowerCAmelCase            = torch.Size([1, 14_08, 15_36]  )
										__lowerCAmelCase            = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]  )
					elif model_name == "videomae-base-finetuned-ssv2":
										__lowerCAmelCase            = torch.Size([1, 1_74]  )
										__lowerCAmelCase            = torch.tensor([0.19_61, -0.83_37, -0.63_89]  )
					else:
										raise ValueError(F"""Model name not supported. Should be one of {model_names}"""  )

					# verify logits
					assert logits.shape == expected_shape
					if "finetuned" in model_name:
										assert torch.allclose(logits[0, :3]   ,   SCREAMING_SNAKE_CASE_   ,   atol=1E-4  )
					else:
										print("Logits:"   ,   logits[0, :3, :3]  )
										assert torch.allclose(logits[0, :3, :3]   ,   SCREAMING_SNAKE_CASE_   ,   atol=1E-4  )
					print("Logits ok!"  )

					# verify loss, if applicable
					if model_name == "videomae-base-short":
										__lowerCAmelCase            = outputs.loss
										assert torch.allclose(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   atol=1E-4  )
										print("Loss ok!"  )

					if pytorch_dump_folder_path is not None:
										print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""  )
										image_processor.save_pretrained(SCREAMING_SNAKE_CASE_  )
										model.save_pretrained(SCREAMING_SNAKE_CASE_  )

					if push_to_hub:
										print("Pushing to the hub..."  )
										model.push_to_hub(SCREAMING_SNAKE_CASE_   ,   organization="nielsr"  )


if __name__ == "__main__":
					UpperCamelCase__					    =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
					    type=str,
					    help=(
					        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
					        """ download link."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					UpperCamelCase__					    =    parser.parse_args()
					convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




