

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        __UpperCamelCase     (      _A					:							Tuple		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=TimesformerConfig()

							if "large" in model_name:
														lowerCamelCase_								=96

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

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

							return config




def        __UpperCamelCase     (      _A					:							int		)				->str:



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

							return name




def        __UpperCamelCase     (      _A					:							Any      ,  _A					:							Optional[int]		)				->List[Any]:



							"""simple docstring"""
							for key in orig_state_dict.copy().keys():
														lowerCamelCase_								=orig_state_dict.pop(_A		)

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

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

							return orig_state_dict




def        __UpperCamelCase     (      )				->Union[str, Any]:



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




def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							str      ,  _A					:							Any      ,  _A					:							Optional[int]		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=get_timesformer_config(_A		)

							lowerCamelCase_								=TimesformerForVideoClassification(_A		)

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

							model.load_state_dict(_A		)
							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(video[:8]      ,  return_tensors="""pt"""		)

							lowerCamelCase_								=model(**_A		)
							lowerCamelCase_								=outputs.logits

							lowerCamelCase_								=[
							    # 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":
														lowerCamelCase_								=torch.Size([1, 400]		)
														lowerCamelCase_								=torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]		)
							elif model_name == "timesformer-base-finetuned-k600":
														lowerCamelCase_								=torch.Size([1, 600]		)
														lowerCamelCase_								=torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]		)
							elif model_name == "timesformer-base-finetuned-ssv2":
														lowerCamelCase_								=torch.Size([1, 174]		)
														lowerCamelCase_								=torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]		)
							elif model_name == "timesformer-large-finetuned-k400":
														lowerCamelCase_								=torch.Size([1, 400]		)
														lowerCamelCase_								=torch.tensor([0, 0, 0]		)
							elif model_name == "timesformer-large-finetuned-k600":
														lowerCamelCase_								=torch.Size([1, 600]		)
														lowerCamelCase_								=torch.tensor([0, 0, 0]		)
							elif model_name == "timesformer-large-finetuned-ssv2":
														lowerCamelCase_								=torch.Size([1, 174]		)
														lowerCamelCase_								=torch.tensor([0, 0, 0]		)
							elif model_name == "timesformer-hr-finetuned-k400":
														lowerCamelCase_								=torch.Size([1, 400]		)
														lowerCamelCase_								=torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]		)
							elif model_name == "timesformer-hr-finetuned-k600":
														lowerCamelCase_								=torch.Size([1, 600]		)
														lowerCamelCase_								=torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]		)
							elif model_name == "timesformer-hr-finetuned-ssv2":
														lowerCamelCase_								=torch.Size([1, 174]		)
														lowerCamelCase_								=torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]		)
							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__":
	__A	:   Tuple	=					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.'
	)

	__A	:   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()
__A	:   Optional[Any]	=					logging.get_logger(__name__)

__A	:   Optional[int]	=					{
    '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',
}
__A	:   Optional[int]	=					[
    'lm_head',
]




def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Dict      ,  _A					:							Tuple      ,  _A					:							str      ,  _A					:							int		)				->List[str]:



							"""simple docstring"""
							for attribute in key.split("""."""		):
														lowerCamelCase_								=getattr(_A      ,  _A		)

							if weight_type is not None:
														lowerCamelCase_								=getattr(_A      ,  _A		).shape
							else:
														lowerCamelCase_								=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":
														lowerCamelCase_								=value
							elif weight_type == "weight_g":
														lowerCamelCase_								=value
							elif weight_type == "weight_v":
														lowerCamelCase_								=value
							elif weight_type == "bias":
														lowerCamelCase_								=value
							else:
														lowerCamelCase_								=value

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




def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Dict      ,  _A					:							Optional[Any]		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=[]
							lowerCamelCase_								=fairseq_model.state_dict()

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

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

							for name, value in fairseq_dict.items():
														lowerCamelCase_								=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    _A      ,  _A      ,  _A      ,  _A      ,  )
																					lowerCamelCase_								=True
														else:
																					for key, mapped_key in MAPPING.items():
																												if not is_headless:
																																			lowerCamelCase_								="""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]:
																																			lowerCamelCase_								=True
																																			if "*" in mapped_key:
																																										lowerCamelCase_								=name.split(_A		)[0].split("""."""		)[-2]
																																										lowerCamelCase_								=mapped_key.replace("""*"""      ,  _A		)
																																			if "weight_g" in name:
																																										lowerCamelCase_								="""weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase_								="""weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase_								="""bias"""
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCamelCase_								="""weight"""
																																			else:
																																										lowerCamelCase_								=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        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Union[str, Any]		)				->Tuple:



							"""simple docstring"""
							lowerCamelCase_								=path.split("""."""		)
							return reduce(_A      ,  _A      ,  _A		)




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							List[Any]      ,  _A					:							str      ,  _A					:							Dict		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=access_by_string(_A      ,  _A		)
							lowerCamelCase_								=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.'		)
							lowerCamelCase_								=fsq_value
							logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->Union[str, Any]:



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

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

							set_weights(_A      ,  _A      ,  _A      ,  f'conv_layers.{layer_id}.{layer_type}.{weight_type}'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Optional[int]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]		)				->Tuple:



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

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

							set_weights(_A      ,  _A      ,  _A      ,  f'layers.{layer_id}.{layer_type}.{weight_type}'		)




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							Optional[Any]      ,  _A					:							Optional[int]      ,  _A					:							List[Any]=None      ,  _A					:							int=None      ,  _A					:							Union[str, Any]=True		)				->str:



							"""simple docstring"""
							if config_path is not None:
														lowerCamelCase_								=DataaVecAudioConfig.from_pretrained(_A		)
							else:
														lowerCamelCase_								=DataaVecAudioConfig()

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

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

							def load_dataavec(_A					:							str		):
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
														return model[0].eval()

							lowerCamelCase_								=load_dataavec(_A		)

							recursively_load_weights(_A      ,  _A      ,  not is_finetuned		)

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

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

							lowerCamelCase_								=processor(_A      ,  return_tensors="""pt"""      ,  padding=_A		)

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

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

														lowerCamelCase_								=torch.argmax(_A      ,  dim=-1		)
														lowerCamelCase_								=processor.batch_decode(_A		)

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

							print(our_output.shape      ,  their_output.shape		)
							lowerCamelCase_								=torch.max(torch.abs(our_output - their_output		)		).item()
							print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
							lowerCamelCase_								=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__":
	__A	:   Optional[Any]	=					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'
	)
	__A	:   List[str]	=					parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)
