




import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor




def       __magic_name__    (			A		: Optional[int]       ):






			'''simple docstring'''







			a   =      TimesformerConfig()

			if "large" in model_name:
						a   =      96

			if "hr" in model_name:
						a   =      16
						a   =      448

			a   =      "huggingface/label-files"
			if "k400" in model_name:
						a   =      400
						a   =      "kinetics400-id2label.json"
			elif "k600" in model_name:
						a   =      600
						a   =      "kinetics600-id2label.json"
			elif "ssv2" in model_name:
						a   =      174
						a   =      "something-something-v2-id2label.json"
			else:
						raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."       )
			a   =      json.load(open(hf_hub_download(A,  A,  repo_type="dataset"       ),  "r"       )       )
			a   =      {int(A       ): v for k, v in idalabel.items()}
			a   =      idalabel
			a   =      {v: k for k, v in idalabel.items()}

			return config




def       __magic_name__    (			A		: Union[str, Any]       ):






			'''simple docstring'''







			if "encoder." in name:
						a   =      name.replace("encoder.",  ""       )
			if "cls_token" in name:
						a   =      name.replace("cls_token",  "timesformer.embeddings.cls_token"       )
			if "pos_embed" in name:
						a   =      name.replace("pos_embed",  "timesformer.embeddings.position_embeddings"       )
			if "time_embed" in name:
						a   =      name.replace("time_embed",  "timesformer.embeddings.time_embeddings"       )
			if "patch_embed.proj" in name:
						a   =      name.replace("patch_embed.proj",  "timesformer.embeddings.patch_embeddings.projection"       )
			if "patch_embed.norm" in name:
						a   =      name.replace("patch_embed.norm",  "timesformer.embeddings.norm"       )
			if "blocks" in name:
						a   =      name.replace("blocks",  "timesformer.encoder.layer"       )
			if "attn.proj" in name:
						a   =      name.replace("attn.proj",  "attention.output.dense"       )
			if "attn" in name and "bias" not in name and "temporal" not in name:
						a   =      name.replace("attn",  "attention.self"       )
			if "attn" in name and "temporal" not in name:
						a   =      name.replace("attn",  "attention.attention"       )
			if "temporal_norm1" in name:
						a   =      name.replace("temporal_norm1",  "temporal_layernorm"       )
			if "temporal_attn.proj" in name:
						a   =      name.replace("temporal_attn",  "temporal_attention.output.dense"       )
			if "temporal_fc" in name:
						a   =      name.replace("temporal_fc",  "temporal_dense"       )
			if "norm1" in name and "temporal" not in name:
						a   =      name.replace("norm1",  "layernorm_before"       )
			if "norm2" in name:
						a   =      name.replace("norm2",  "layernorm_after"       )
			if "mlp.fc1" in name:
						a   =      name.replace("mlp.fc1",  "intermediate.dense"       )
			if "mlp.fc2" in name:
						a   =      name.replace("mlp.fc2",  "output.dense"       )
			if "norm.weight" in name and "fc" not in name and "temporal" not in name:
						a   =      name.replace("norm.weight",  "timesformer.layernorm.weight"       )
			if "norm.bias" in name and "fc" not in name and "temporal" not in name:
						a   =      name.replace("norm.bias",  "timesformer.layernorm.bias"       )
			if "head" in name:
						a   =      name.replace("head",  "classifier"       )

			return name




def       __magic_name__    (			A		: Tuple,  A		: List[str]       ):






			'''simple docstring'''







			for key in orig_state_dict.copy().keys():
						a   =      orig_state_dict.pop(A       )

						if key.startswith("model."       ):
									a   =      key.replace("model.",  ""       )

						if "qkv" in key:
									a   =      key.split("."       )
									a   =      int(key_split[1]       )
									a   =      "timesformer.encoder.layer."
									if "temporal" in key:
												a   =      ".temporal_attention.attention.qkv."
									else:
												a   =      ".attention.attention.qkv."
									if "weight" in key:
												a   =      val
									else:
												a   =      val
						else:
									a   =      val

			return orig_state_dict




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video",  filename="eating_spaghetti.npy",  repo_type="dataset"       )
			a   =      np.load(A       )
			return list(A       )




def       __magic_name__    (			A		: Optional[Any],  A		: Tuple,  A		: str,  A		: int       ):






			'''simple docstring'''







			a   =      get_timesformer_config(A       )

			a   =      TimesformerForVideoClassification(A       )

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

			model.load_state_dict(A       )
			model.eval()

			# verify model on basic input
			a   =      VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],  image_std=[0.5, 0.5, 0.5]       )
			a   =      prepare_video()
			a   =      image_processor(video[:8],  return_tensors="pt"       )

			a   =      model(**A       )
			a   =      outputs.logits

			a   =      [
			    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-k400",
			    "timesformer-large-finetuned-k400",
			    "timesformer-hr-finetuned-k400",
			    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-k600",
			    "timesformer-large-finetuned-k600",
			    "timesformer-hr-finetuned-k600",
			    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
			    "timesformer-base-finetuned-ssv2",
			    "timesformer-large-finetuned-ssv2",
			    "timesformer-hr-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 == "timesformer-base-finetuned-k400":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([-0.30_16, -0.77_13, -0.42_05]       )
			elif model_name == "timesformer-base-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([-0.72_67, -0.74_66, 3.24_04]       )
			elif model_name == "timesformer-base-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      torch.tensor([-0.90_59, 0.64_33, -3.14_57]       )
			elif model_name == "timesformer-large-finetuned-k400":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-large-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-large-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      torch.tensor([0, 0, 0]       )
			elif model_name == "timesformer-hr-finetuned-k400":
						a   =      torch.Size([1, 400]       )
						a   =      torch.tensor([-0.96_17, -3.73_11, -3.77_08]       )
			elif model_name == "timesformer-hr-finetuned-k600":
						a   =      torch.Size([1, 600]       )
						a   =      torch.tensor([2.52_73, 0.71_27, 1.88_48]       )
			elif model_name == "timesformer-hr-finetuned-ssv2":
						a   =      torch.Size([1, 174]       )
						a   =      torch.tensor([-3.67_56, -0.75_13, 0.71_80]       )
			else:
						raise ValueError(F"""Model name not supported. Should be one of {model_names}"""       )

			# verify logits
			assert logits.shape == expected_shape
			assert torch.allclose(logits[0, :3],  A,  atol=1E-4       )
			print("Logits 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(A       )
						model.save_pretrained(A       )

			if push_to_hub:
						print("Pushing to the hub..."       )
						model.push_to_hub(F"""fcakyon/{model_name}"""       )


if __name__ == "__main__":
		__lowerCAmelCase		:    Union[str, Any]	      =		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download',
		    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='',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--model_name', default='timesformer-base-finetuned-k400', 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.'
		)

		__lowerCAmelCase		:    Optional[Any]	      =		parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)












import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__lowerCAmelCase		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__lowerCAmelCase		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			for attribute in key.split("."       ):
						a   =      getattr(A,  A       )

			if weight_type is not None:
						a   =      getattr(A,  A       ).shape
			else:
						a   =      hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""       )

			if weight_type == "weight":
						a   =      value
			elif weight_type == "weight_g":
						a   =      value
			elif weight_type == "weight_v":
						a   =      value
			elif weight_type == "bias":
						a   =      value
			else:
						a   =      value

			logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






			'''simple docstring'''







			a   =      []
			a   =      fairseq_model.state_dict()

			if not is_headless:
						a   =      hf_model.dataavec_audio.feature_extractor
						a   =      hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						a   =      hf_model.feature_extractor
						a   =      hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						a   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															a   =      "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
															a   =      True
															if "*" in mapped_key:
																		a   =      name.split(A       )[0].split("."       )[-2]
																		a   =      mapped_key.replace("*",  A       )
															if "weight_g" in name:
																		a   =      "weight_g"
															elif "weight_v" in name:
																		a   =      "weight_v"
															elif "bias" in name:
																		a   =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a   =      "weight"
															else:
																		a   =      None
															set_recursively(A,  A,  A,  A,  A       )
												continue
						if not is_used:
									unused_weights.append(A       )

			logger.warning(F"""Unused weights: {unused_weights}"""       )




def       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      access_by_string(A,  A       )
			a   =      hf_weight.data

			if fsq_value.shape != hf_value.shape:
						raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""       )
			a   =      fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      full_name.split("conv_layers."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id == 0:
						a   =      "conv"
			elif type_id == 2:
						a   =      "layer_norm"
			else:
						unused_weights.append(A       )
						return

			set_weights(A,  A,  A,  F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''simple docstring'''







			a   =      full_name.split("pos_conv."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id != 0:
						unused_weights.append(A       )
						return
			else:
						a   =      "conv"

			set_weights(A,  A,  A,  F"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			if config_path is not None:
						a   =      DataaVecAudioConfig.from_pretrained(A       )
			else:
						a   =      DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						a   =      DataaVecAudioModel(A       )
						a   =      os.path.dirname(A       )

						a   =      torch.load(A       )
						a   =      state_dict["model"].pop("final_proj.0.weight"       )
						a   =      state_dict["model"].pop("final_proj.0.bias"       )
						a   =      os.path.join(A,  "converted.pt"       )
						torch.save(A,  A       )
			else:
						a   =      DataaVecAudioForCTC(A       )
						a   =      checkpoint_path

			def load_dataavec(A		: Optional[Any]       ):
						a   ,		a   ,		a   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
						return model[0].eval()

			a   =      load_dataavec(A       )

			recursively_load_weights(A,  A,  not is_finetuned       )

			a   =      WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"       )

			a   =      load_dataset("patrickvonplaten/librispeech_asr_dummy",  "clean",  split="validation"       )
			a   =      [x["array"] for x in ds[:4]["audio"]]

			a   =      processor(A,  return_tensors="pt",  padding=A       )

			a   =      inputs.input_values
			a   =      inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

			hf_wavavec.eval()
			model.eval()
			if is_finetuned:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "encoder_out"
						].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["logits"]

						a   =      torch.argmax(A,  dim=-1       )
						a   =      processor.batch_decode(A       )

						print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""       )
			else:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "layer_results"
						][-1][0].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["last_hidden_state"]

			print(our_output.shape,  their_output.shape       )
			a   =      torch.max(torch.abs(our_output - their_output       )       ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
			a   =      torch.allclose(A,  A,  atol=1E-3       )
			print("Do both models output the same tensors?",  "🔥" if success else "💩"       )
			if not success:
						raise Exception("Something went wRoNg"       )

			hf_wavavec.save_pretrained(A       )

			if is_finetuned:
						processor.save_pretrained(A       )
			else:
						processor.feature_extractor.save_pretrained(A       )


if __name__ == "__main__":
		__lowerCAmelCase		:    List[str]	      =		argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		__lowerCAmelCase		:    int	      =		parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)






