





"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase    :	Optional[int]						=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase    :	Any						=     []
for i in range(6):
       # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
       )
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
       )
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
       # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
       )
       rename_keys.append(
           (
               f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
               f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
           )
       )
       rename_keys.append(
           (
               f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
               f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
           )
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Dict     ,    _UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						Optional[Any] ):
 lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
 lowerCAmelCase													=   val

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Union[str, Any] ):
 lowerCAmelCase													=   OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowerCAmelCase													=   key.replace('backbone.0.body'     ,    'backbone.conv_encoder.model' )
   lowerCAmelCase													=   value
  else:
   lowerCAmelCase													=   value

 return new_state_dict

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Any     ,    _UpperCAmelCase				:						int=False ):
 lowerCAmelCase													=   ''
 if is_panoptic:
  lowerCAmelCase													=   'detr.'

 # first: transformer encoder
 for i in range(6 ):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
  # next, add query, keys and values (in that order) to the state dict
  lowerCAmelCase													=   in_proj_weight[:256, :]
  lowerCAmelCase													=   in_proj_bias[:256]
  lowerCAmelCase													=   in_proj_weight[256:512, :]
  lowerCAmelCase													=   in_proj_bias[256:512]
  lowerCAmelCase													=   in_proj_weight[-256:, :]
  lowerCAmelCase													=   in_proj_bias[-256:]
 # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
 for i in range(6 ):
  # read in weights + bias of input projection layer of self-attention
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
  # next, add query, keys and values (in that order) to the state dict
  lowerCAmelCase													=   in_proj_weight[:256, :]
  lowerCAmelCase													=   in_proj_bias[:256]
  lowerCAmelCase													=   in_proj_weight[256:512, :]
  lowerCAmelCase													=   in_proj_bias[256:512]
  lowerCAmelCase													=   in_proj_weight[-256:, :]
  lowerCAmelCase													=   in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  lowerCAmelCase													=   state_dict.pop(
      F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight' )
  lowerCAmelCase													=   state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias' )
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  lowerCAmelCase													=   in_proj_weight_cross_attn[:256, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[:256]
  lowerCAmelCase													=   in_proj_weight_cross_attn[256:512, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[256:512]
  lowerCAmelCase													=   in_proj_weight_cross_attn[-256:, :]
  lowerCAmelCase													=   in_proj_bias_cross_attn[-256:]

def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase													=   Image.open(requests.get(_UpperCAmelCase     ,    stream=_UpperCAmelCase ).raw )

 return im

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Dict     ,    _UpperCAmelCase				:						List[str] ):
 lowerCAmelCase													=   DetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  lowerCAmelCase													=   'resnet101'
 if "dc5" in model_name:
  lowerCAmelCase													=   True
 lowerCAmelCase													=   'panoptic' in model_name
 if is_panoptic:
  lowerCAmelCase													=   250
 else:
  lowerCAmelCase													=   91
  lowerCAmelCase													=   'huggingface/label-files'
  lowerCAmelCase													=   'coco-detection-id2label.json'
  lowerCAmelCase													=   json.load(open(hf_hub_download(_UpperCAmelCase     ,    _UpperCAmelCase     ,    repo_type='dataset' )     ,    'r' ) )
  lowerCAmelCase													=   {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
  lowerCAmelCase													=   idalabel
  lowerCAmelCase													=   {v: k for k, v in idalabel.items()}

 # load image processor
 lowerCAmelCase													=   'coco_panoptic' if is_panoptic else 'coco_detection'
 lowerCAmelCase													=   DetrImageProcessor(format=_UpperCAmelCase )

 # prepare image
 lowerCAmelCase													=   prepare_img()
 lowerCAmelCase													=   image_processor(images=_UpperCAmelCase     ,    return_tensors='pt' )
 lowerCAmelCase													=   encoding['pixel_values']

 logger.info(F'Converting model {model_name}...' )

 # load original model from torch hub
 lowerCAmelCase													=   torch.hub.load('facebookresearch/detr'     ,    _UpperCAmelCase     ,    pretrained=_UpperCAmelCase ).eval()
 lowerCAmelCase													=   detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   lowerCAmelCase													=   'detr.' + src
  rename_key(_UpperCAmelCase     ,    _UpperCAmelCase     ,    _UpperCAmelCase )
 lowerCAmelCase													=   rename_backbone_keys(_UpperCAmelCase )
 # query, key and value matrices need special treatment
 read_in_q_k_v(_UpperCAmelCase     ,    is_panoptic=_UpperCAmelCase )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowerCAmelCase													=   'detr.model.' if is_panoptic else 'model.'
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith('detr' )
       and not key.startswith('class_labels_classifier' )
       and not key.startswith('bbox_predictor' )
   ):
    lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
    lowerCAmelCase													=   val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
    lowerCAmelCase													=   val
   elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
    continue
   else:
    lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
    lowerCAmelCase													=   val
  else:
   if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
    lowerCAmelCase													=   state_dict.pop(_UpperCAmelCase )
    lowerCAmelCase													=   val
    # finally, create HuggingFace model and load state dict
 lowerCAmelCase													=   DetrForSegmentation(_UpperCAmelCase ) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase )
 model.load_state_dict(_UpperCAmelCase )
 model.eval()
 # verify our conversion
 lowerCAmelCase													=   detr(_UpperCAmelCase )
 lowerCAmelCase													=   model(_UpperCAmelCase )
 assert torch.allclose(outputs.logits     ,    original_outputs['pred_logits']     ,    atol=1e-4 )
 assert torch.allclose(outputs.pred_boxes     ,    original_outputs['pred_boxes']     ,    atol=1e-4 )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks     ,    original_outputs['pred_masks']     ,    atol=1e-4 )

 # Save model and image processor
 logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
 Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
 model.save_pretrained(_UpperCAmelCase )
 image_processor.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
       __UpperCamelCase    :	Any						=     argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR model you\'d like to convert.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
       )
       __UpperCamelCase    :	List[Any]						=     parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












"""simple docstring"""
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class 			a       :



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   cuda
  lowerCAmelCase													=   AutoConfig.from_pretrained(_snake_case    )
  lowerCAmelCase													=   AutoModelForCTC.from_pretrained(_snake_case    )
  self.model.eval()
  if self.cuda:
   self.model.to(device='cuda'    )
  lowerCAmelCase													=   AutoProcessor.from_pretrained(_snake_case    )
  lowerCAmelCase													=   torchaudio.transforms.Resample(_snake_case							,					1_60_00    )
  lowerCAmelCase													=   0
  lowerCAmelCase													=   list(self.processor.tokenizer.get_vocab().keys()    )
  for i in range(len(_snake_case    )    ):
   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
    lowerCAmelCase													=   i
  print('Blank Token id [PAD]/<pad>'							,					_snake_case    )
  lowerCAmelCase													=   blank_id



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase													=   torchaudio.load(_snake_case    )
  lowerCAmelCase													=   self.resampler(_snake_case    ).squeeze().numpy()
  return speech



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






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

  lowerCAmelCase													=   self.speech_file_to_array_fn(item['wav_path']    )
  lowerCAmelCase													=   self.processor(_snake_case							,					sampling_rate=1_60_00							,					return_tensors='pt'							,					padding=_snake_case    )
  if self.cuda:
   lowerCAmelCase													=   inputs.to(device='cuda'    )

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

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

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

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

  def get_trellis(_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   emission.size(0    )
   lowerCAmelCase													=   len(_snake_case    )

   # 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.
   lowerCAmelCase													=   torch.full((num_frames + 1, num_tokens + 1)							,					-float('inf'    )    )
   lowerCAmelCase													=   0
   for t in range(_snake_case    ):
    lowerCAmelCase													=   torch.maximum(
        # Score for staying at the same token
        trellis[t, 1:] + emission[t, blank_id]							,					trellis[t, :-1] + emission[t, tokens]							,					)
   return trellis

  lowerCAmelCase													=   get_trellis(_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42

  def backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   trellis.size(1    ) - 1
   lowerCAmelCase													=   torch.argmax(trellis[:, j]    ).item()

   lowerCAmelCase													=   []
   for t in range(_snake_case							,					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.
    lowerCAmelCase													=   trellis[t - 1, j] + emission[t - 1, blank_id]
    # Score for token changing from C-1 at T-1 to J at T.
    lowerCAmelCase													=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

    # 2. Store the path with frame-wise probability.
    lowerCAmelCase													=   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							,					_snake_case    )    )

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

  lowerCAmelCase													=   backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42



   def __repr__(    self    ):







    """simple docstring"""






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



   @property
   def       UpperCamelCase__     (    self    ):







    """simple docstring"""






    return self.end - self.start

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

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



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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

  # load text file
  lowerCAmelCase													=   open(_snake_case							,					encoding='utf8'    ).readlines()

  lowerCAmelCase													=   []
  for line in lines:
   if len(line.strip().split('\t'    )    ) != 2:
    print('Script must be in format: 00001  this is my sentence'    )
    exit()

   lowerCAmelCase							,lowerCAmelCase													=   line.strip().split('\t'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.wav'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.txt'    )

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

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


def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   argparse.ArgumentParser()

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

 lowerCAmelCase													=   parser.parse_args()

 lowerCAmelCase													=   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()





