




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      : List[Any]					      =   logging.get_logger(__name__)





def 	__A	(			__lowerCamelCase					)   -> Optional[int]:
 a		       =							ConvNextConfig()

 if "tiny" in checkpoint_url:
  a		       =							[3, 3, 9, 3]
  a		       =							[96, 192, 384, 768]
 if "small" in checkpoint_url:
  a		       =							[3, 3, 27, 3]
  a		       =							[96, 192, 384, 768]
 if "base" in checkpoint_url:
  a		       =							[3, 3, 27, 3]
  a		       =							[128, 256, 512, 1024]
 if "large" in checkpoint_url:
  a		       =							[3, 3, 27, 3]
  a		       =							[192, 384, 768, 1536]
 if "xlarge" in checkpoint_url:
  a		       =							[3, 3, 27, 3]
  a		       =							[256, 512, 1024, 2048]

 if "1k" in checkpoint_url:
  a		       =							1000
  a		       =							"""imagenet-1k-id2label.json"""
  a		       =							(1, 1000)
 else:
  a		       =							2_1841
  a		       =							"""imagenet-22k-id2label.json"""
  a		       =							(1, 2_1841)

 a		       =							"""huggingface/label-files"""
 a		       =							num_labels
 a		       =							json.load(open(hf_hub_download(__lowerCamelCase							,    __lowerCamelCase							,    repo_type="""dataset"""					)							,    """r"""					)					)
 a		       =							{int(__lowerCamelCase					): v for k, v in idalabel.items()}
 if "1k" not in checkpoint_url:
  # this dataset contains 21843 labels but the model only has 21841
  # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  del idalabel[9205]
  del idalabel[1_5027]
 a		       =							idalabel
 a		       =							{v: k for k, v in idalabel.items()}
 a		       =							hidden_sizes
 a		       =							depths

 return config, expected_shape





def 	__A	(			__lowerCamelCase					)   -> int:
 if "downsample_layers.0.0" in name:
  a		       =							name.replace("""downsample_layers.0.0"""							,    """embeddings.patch_embeddings"""					)
 if "downsample_layers.0.1" in name:
  a		       =							name.replace("""downsample_layers.0.1"""							,    """embeddings.norm"""					)  # we rename to layernorm later on
 if "downsample_layers.1.0" in name:
  a		       =							name.replace("""downsample_layers.1.0"""							,    """stages.1.downsampling_layer.0"""					)
 if "downsample_layers.1.1" in name:
  a		       =							name.replace("""downsample_layers.1.1"""							,    """stages.1.downsampling_layer.1"""					)
 if "downsample_layers.2.0" in name:
  a		       =							name.replace("""downsample_layers.2.0"""							,    """stages.2.downsampling_layer.0"""					)
 if "downsample_layers.2.1" in name:
  a		       =							name.replace("""downsample_layers.2.1"""							,    """stages.2.downsampling_layer.1"""					)
 if "downsample_layers.3.0" in name:
  a		       =							name.replace("""downsample_layers.3.0"""							,    """stages.3.downsampling_layer.0"""					)
 if "downsample_layers.3.1" in name:
  a		       =							name.replace("""downsample_layers.3.1"""							,    """stages.3.downsampling_layer.1"""					)
 if "stages" in name and "downsampling_layer" not in name:
  # stages.0.0. for instance should be renamed to stages.0.layers.0.
  a		       =							name[: len("""stages.0"""					)] + """.layers""" + name[len("""stages.0"""					) :]
 if "stages" in name:
  a		       =							name.replace("""stages"""							,    """encoder.stages"""					)
 if "norm" in name:
  a		       =							name.replace("""norm"""							,    """layernorm"""					)
 if "gamma" in name:
  a		       =							name.replace("""gamma"""							,    """layer_scale_parameter"""					)
 if "head" in name:
  a		       =							name.replace("""head"""							,    """classifier"""					)

 return name





def 	__A	(			)   -> Optional[Any]:
 a		       =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 a		       =							Image.open(requests.get(__lowerCamelCase							,    stream=__lowerCamelCase					).raw					)
 return im





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
 a       ,				a		       =							get_convnext_config(__lowerCamelCase					)
 # load original state_dict from URL
 a		       =							torch.hub.load_state_dict_from_url(__lowerCamelCase					)["""model"""]
 # rename keys
 for key in state_dict.copy().keys():
  a		       =							state_dict.pop(__lowerCamelCase					)
  a		       =							val
 # add prefix to all keys expect classifier head
 for key in state_dict.copy().keys():
  a		       =							state_dict.pop(__lowerCamelCase					)
  if not key.startswith("""classifier"""					):
   a		       =							"""convnext.""" + key
  a		       =							val

 # load HuggingFace model
 a		       =							ConvNextForImageClassification(__lowerCamelCase					)
 model.load_state_dict(__lowerCamelCase					)
 model.eval()

 # Check outputs on an image, prepared by ConvNextImageProcessor
 a		       =							224 if """224""" in checkpoint_url else 384
 a		       =							ConvNextImageProcessor(size=__lowerCamelCase					)
 a		       =							image_processor(images=prepare_img()							,    return_tensors="""pt"""					).pixel_values

 a		       =							model(__lowerCamelCase					).logits

 # note: the logits below were obtained without center cropping
 if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
  a		       =							torch.tensor([-0.1210, -0.6605, 0.1918]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  a		       =							torch.tensor([-0.4473, -0.1847, -0.6365]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  a		       =							torch.tensor([0.4525, 0.7539, 0.0308]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  a		       =							torch.tensor([0.3561, 0.6350, -0.0384]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  a		       =							torch.tensor([0.4174, -0.0989, 0.1489]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  a		       =							torch.tensor([0.2513, -0.1349, -0.1613]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  a		       =							torch.tensor([1.2980, 0.3631, -0.1198]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  a		       =							torch.tensor([1.2963, 0.1227, 0.1723]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  a		       =							torch.tensor([1.7956, 0.8390, 0.2820]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  a		       =							torch.tensor([-0.2822, -0.0502, -0.0878]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  a		       =							torch.tensor([-0.5672, -0.0730, -0.4348]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  a		       =							torch.tensor([0.2681, 0.2365, 0.6246]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  a		       =							torch.tensor([-0.2642, 0.3931, 0.5116]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  a		       =							torch.tensor([-0.6677, -0.1873, -0.8379]					)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  a		       =							torch.tensor([-0.7749, -0.2967, -0.6444]					)
 else:
  raise ValueError(f'Unknown URL: {checkpoint_url}'					)

 assert torch.allclose(logits[0, :3]							,    __lowerCamelCase							,    atol=1E-3					)
 assert logits.shape == expected_shape

 Path(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
 print(f'Saving model to {pytorch_dump_folder_path}'					)
 model.save_pretrained(__lowerCamelCase					)
 print(f'Saving image processor to {pytorch_dump_folder_path}'					)
 image_processor.save_pretrained(__lowerCamelCase					)

 print("""Pushing model to the hub..."""					)
 a		       =							"""convnext"""
 if "tiny" in checkpoint_url:
  model_name += "-tiny"
 elif "small" in checkpoint_url:
  model_name += "-small"
 elif "base" in checkpoint_url:
  model_name += "-base"
 elif "xlarge" in checkpoint_url:
  model_name += "-xlarge"
 elif "large" in checkpoint_url:
  model_name += "-large"
 if "224" in checkpoint_url:
  model_name += "-224"
 elif "384" in checkpoint_url:
  model_name += "-384"
 if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  model_name += "-22k"
 if "22k" in checkpoint_url and "1k" in checkpoint_url:
  model_name += "-22k-1k"

 model.push_to_hub(
     repo_path_or_name=Path(__lowerCamelCase							,    __lowerCamelCase					)							,    organization="""nielsr"""							,    commit_message="""Add model"""							,    )


if __name__ == "__main__":
  __UpperCamelCase      : int					      =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
      type=str,
      help="URL of the original ConvNeXT checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the output PyTorch model directory.",
  )

  __UpperCamelCase      : List[Any]					      =   parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 	__lowerCAmelCase			:
							def __init__(		self		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							cuda
								a		       =							AutoConfig.from_pretrained(__magic_name__       )
								a		       =							AutoModelForCTC.from_pretrained(__magic_name__       )
								self.model.eval()
								if self.cuda:
									self.model.to(device="""cuda"""       )
								a		       =							AutoProcessor.from_pretrained(__magic_name__       )
								a		       =							torchaudio.transforms.Resample(__magic_name__	,   1_6000       )
								a		       =							0
								a		       =							list(self.processor.tokenizer.get_vocab().keys()       )
								for i in range(len(__magic_name__       )       ):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										a		       =							i
								print("""Blank Token id [PAD]/<pad>"""	,   __magic_name__       )
								a		       =							blank_id
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a       ,				a		       =							torchaudio.load(__magic_name__       )
								a		       =							self.resampler(__magic_name__       ).squeeze().numpy()
								return speech
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a		       =							self.blank_id
								a		       =							"""|""".join(item["""sent"""].split(""" """       )       )
								if not os.path.isfile(item["""wav_path"""]       ):
									print(item["""wav_path"""]	,   """not found in wavs directory"""       )

								a		       =							self.speech_file_to_array_fn(item["""wav_path"""]       )
								a		       =							self.processor(__magic_name__	,   sampling_rate=1_6000	,   return_tensors="""pt"""	,   padding=__magic_name__       )
								if self.cuda:
									a		       =							inputs.to(device="""cuda"""       )

								with torch.no_grad():
									a		       =							self.model(inputs.input_values       ).logits

								# get the emission probability at frame level
								a		       =							torch.log_softmax(__magic_name__	,   dim=-1       )
								a		       =							emissions[0].cpu().detach()

								# get labels from vocab
								a		       =							([""""""] + list(self.processor.tokenizer.get_vocab().keys()       ))[
								    :-1
								]  # logits don't align with the tokenizer's vocab

								a		       =							{c: i for i, c in enumerate(__magic_name__       )}
								a		       =							[]
								for c in transcript:
									if c in dictionary:
										tokens.append(dictionary[c]       )

								def get_trellis(__magic_name__		:Optional[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:Any=0       ):
									a		       =							emission.size(0       )
									a		       =							len(__magic_name__       )

									# Trellis has extra diemsions for both time axis and tokens.
									# The extra dim for tokens represents <SoS> (start-of-sentence)
									# The extra dim for time axis is for simplification of the code.
									a		       =							torch.full((num_frames + 1, num_tokens + 1)	,   -float("""inf"""       )       )
									a		       =							0
									for t in range(__magic_name__       ):
										a		       =							torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]	,   trellis[t, :-1] + emission[t, tokens]	,   )
									return trellis

								a		       =							get_trellis(__magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42

								def backtrack(__magic_name__		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:int	,   __magic_name__		:str=0       ):
									a		       =							trellis.size(1       ) - 1
									a		       =							torch.argmax(trellis[:, j]       ).item()

									a		       =							[]
									for t in range(__magic_name__	,   0	,   -1       ):
										# 1. Figure out if the current position was stay or change
										# Note (again):
										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
										# Score for token staying the same from time frame J-1 to T.
										a		       =							trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										a		       =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										a		       =							emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
										# Return token index and time index in non-trellis coordinate.
										path.append(Point(j - 1	,   t - 1	,   __magic_name__       )       )

										# 3. Update the token
										if changed > stayed:
											j -= 1
											if j == 0:
												break
									else:
										raise ValueError("""Failed to align"""       )
									return path[::-1]

								a		       =							backtrack(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															def __repr__(		self		:Union[str, Any]       ):







																'''simple docstring'''







																return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'





															@property
															def    lowerCamelCase__							(		self		:int       ):







																'''simple docstring'''







																return self.end - self.start

								def merge_repeats(__magic_name__		:int       ):
									a       ,				a		       =							0, 0
									a		       =							[]
									while ia < len(__magic_name__       ):
										while ia < len(__magic_name__       ) and path[ia].token_index == path[ia].token_index:
											ia += 1
										a		       =							sum(path[k].score for k in range(__magic_name__	,   __magic_name__       )       ) / (ia - ia)
										segments.append(
										    Segment(
										        transcript[path[ia].token_index]	,   path[ia].time_index	,   path[ia - 1].time_index + 1	,   __magic_name__	,   )       )
										a		       =							ia
									return segments

								a		       =							merge_repeats(__magic_name__       )
								with open(item["""out_path"""]	,   """w"""       ) as out_align:
									for seg in segments:
										out_align.write(str(__magic_name__       ) + """\n"""       )





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







								'''simple docstring'''







								if not os.path.exists(__magic_name__       ):
									os.makedirs(__magic_name__       )

								# load text file
								a		       =							open(__magic_name__	,   encoding="""utf8"""       ).readlines()

								a		       =							[]
								for line in lines:
									if len(line.strip().split("""\t"""       )       ) != 2:
										print("""Script must be in format: 00001  this is my sentence"""       )
										exit()

									a       ,				a		       =							line.strip().split("""\t"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.wav"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.txt"""       )

									items.append({"""sent""": sentence, """wav_path""": wav_path, """out_path""": out_path}       )
								print("""Number of samples found in script file"""	,   len(__magic_name__       )       )

								for item in tqdm(__magic_name__       ):
									self.align_single_sample(__magic_name__       )







def 	__A	(			)   -> Union[str, Any]:
	a		       =							argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name"""							,    type=__lowerCamelCase							,    default="""arijitx/wav2vec2-xls-r-300m-bengali"""							,    help="""wav2vec model name"""					)
	parser.add_argument("""--wav_dir"""							,    type=__lowerCamelCase							,    default="""./wavs"""							,    help="""directory containing wavs"""					)
	parser.add_argument("""--text_file"""							,    type=__lowerCamelCase							,    default="""script.txt"""							,    help="""file containing text"""					)
	parser.add_argument("""--input_wavs_sr"""							,    type=__lowerCamelCase							,    default=1_6000							,    help="""sampling rate of input audios"""					)
	parser.add_argument(
	    """--output_dir"""							,    type=__lowerCamelCase							,    default="""./out_alignment"""							,    help="""output directory containing the alignment files"""					)
	parser.add_argument("""--cuda"""							,    action="""store_true"""					)

	a		       =							parser.parse_args()

	a		       =							WavaVecaAligner(args.model_name							,    args.input_wavs_sr							,    args.cuda					)
	aligner.align_data(args.wav_dir							,    args.text_file							,    args.output_dir					)


if __name__ == "__main__":
		main()

