import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:






							"""simple docstring"""




							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		pt_weights[f"{prefix}.in_proj_weight"].chunk(3       ,  dim=0							)
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		pt_weights[f"{prefix}.in_proj_bias"].chunk(3       ,  dim=0							)

							lowerCAmelCase     		=		pt_weights[f"{prefix}.out_proj.weight"]
							lowerCAmelCase     		=		pt_weights[f"{prefix}.out_proj.bias"]

							lowerCAmelCase     		=		q_proj
							lowerCAmelCase     		=		q_proj_bias

							lowerCAmelCase     		=		k_proj
							lowerCAmelCase     		=		k_proj_bias

							lowerCAmelCase     		=		v_proj
							lowerCAmelCase     		=		v_proj_bias

							lowerCAmelCase     		=		out_proj_weights
							lowerCAmelCase     		=		out_proj_bias

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Optional[Any]:






							"""simple docstring"""




							copy_linear(hf_mlp.fca       ,  snake_case__       ,  f"{prefix}.c_fc"							)
							copy_linear(hf_mlp.fca       ,  snake_case__       ,  f"{prefix}.c_proj"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Dict:






							"""simple docstring"""




							lowerCAmelCase     		=		pt_weights[f"{prefix}.weight"].data
							lowerCAmelCase     		=		pt_weights[f"{prefix}.bias"].data

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:






							"""simple docstring"""




							copy_linear(hf_layer.layer_norma       ,  snake_case__       ,  f"{prefix}.ln_1"							)
							copy_linear(hf_layer.layer_norma       ,  snake_case__       ,  f"{prefix}.ln_2"							)

							# copy MLP
							copy_mlp(hf_layer.mlp       ,  snake_case__       ,  f"{prefix}.mlp"							)

							# copy attn
							copy_attn_layer(hf_layer.self_attn       ,  snake_case__       ,  f"{prefix}.attn"							)

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






							"""simple docstring"""




							for layer_id, hf_layer in enumerate(snake_case__							):
														copy_layer(snake_case__       ,  snake_case__       ,  f"{prefix}.{layer_id}"							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Optional[int]:






							"""simple docstring"""




							lowerCAmelCase     		=		pt_weights['''text_projection'''].data.T

							# copy text encoder
							for name, param in hf_model.text_model.named_parameters():
														lowerCAmelCase     		=		pt_weights[f"bert.{name}"].data

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     List[Any]:






							"""simple docstring"""




							lowerCAmelCase     		=		pt_weights['''visual.proj'''].data.T

							# copy layer norms
							copy_linear(hf_model.vision_model.pre_layrnorm       ,  snake_case__       ,  '''visual.ln_pre'''							)
							copy_linear(hf_model.vision_model.post_layernorm       ,  snake_case__       ,  '''visual.ln_post'''							)

							# copy embeddings
							lowerCAmelCase     		=		pt_weights['''visual.conv1.weight'''].data
							lowerCAmelCase     		=		pt_weights['''visual.class_embedding'''].data
							lowerCAmelCase     		=		pt_weights['''visual.positional_embedding'''].data

							# copy encoder
							copy_layers(hf_model.vision_model.encoder.layers       ,  snake_case__       ,  '''visual.transformer.resblocks'''							)

@torch.no_grad()
def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__=None							)					->     List[str]:






							"""simple docstring"""




							assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
							lowerCAmelCase     		=		ChineseCLIPConfig.from_pretrained(snake_case__							)

							lowerCAmelCase     		=		ChineseCLIPModel(snake_case__							).eval()

							lowerCAmelCase     		=		torch.load(snake_case__       ,  map_location='''cpu'''							)['''state_dict''']
							lowerCAmelCase     		=		{(name[7:] if name.startswith('''module.'''							) else name): value for name, value in pt_weights.items()}

							copy_text_model_and_projection(snake_case__       ,  snake_case__							)
							copy_vision_model_and_projection(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		pt_weights['''logit_scale'''].data

							hf_model.save_pretrained(snake_case__							)


if __name__ == "__main__":
	lowercase__      :    Tuple          =     argparse.ArgumentParser()
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=str,
	    help='''Path to the output folder storing converted hf PyTorch model.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
	)
	parser.add_argument(
	    '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
	)
	lowercase__      :    List[Any]          =     parser.parse_args()

	convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
	print('''The conversion is finished!''')




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		cuda
								lowerCAmelCase     		=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		AutoModelForCTC.from_pretrained(__SCREAMING_SNAKE_CASE    )
								self.model.eval()
								if self.cuda:
															self.model.to(device='''cuda'''    )
								lowerCAmelCase     		=		AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		torchaudio.transforms.Resample(__SCREAMING_SNAKE_CASE		,      16000    )
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		list(self.processor.tokenizer.get_vocab().keys()    )
								for i in range(len(__SCREAMING_SNAKE_CASE    )    ):
															if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						lowerCAmelCase     		=		i
								print('''Blank Token id [PAD]/<pad>'''		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		blank_id


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		torchaudio.load(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.resampler(__SCREAMING_SNAKE_CASE    ).squeeze().numpy()
								return speech


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->str:
								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(__SCREAMING_SNAKE_CASE		,      sampling_rate=16000		,      return_tensors='''pt'''		,      padding=__SCREAMING_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(__SCREAMING_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(__SCREAMING_SNAKE_CASE    )}
								lowerCAmelCase     		=		[]
								for c in transcript:
															if c in dictionary:
																						tokens.append(dictionary[c]    )

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




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float

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

															lowerCAmelCase     		=		[]
															for t in range(__SCREAMING_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		,      __SCREAMING_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(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : str
									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float


									def __repr__(							self    )						->int:
																return F"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"




									@property
									def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
																return self.end - self.start

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

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




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								if not os.path.exists(__SCREAMING_SNAKE_CASE    ):
															os.makedirs(__SCREAMING_SNAKE_CASE    )

								# load text file
								lowerCAmelCase     		=		open(__SCREAMING_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(__SCREAMING_SNAKE_CASE		,      wav_name + '''.wav'''    )
															lowerCAmelCase     		=		os.path.join(__SCREAMING_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(__SCREAMING_SNAKE_CASE    )    )

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

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name'''       ,  type=snake_case__       ,  default='''arijitx/wav2vec2-xls-r-300m-bengali'''       ,  help='''wav2vec model name'''							)
							parser.add_argument('''--wav_dir'''       ,  type=snake_case__       ,  default='''./wavs'''       ,  help='''directory containing wavs'''							)
							parser.add_argument('''--text_file'''       ,  type=snake_case__       ,  default='''script.txt'''       ,  help='''file containing text'''							)
							parser.add_argument('''--input_wavs_sr'''       ,  type=snake_case__       ,  default=1_6_0_0_0       ,  help='''sampling rate of input audios'''							)
							parser.add_argument(
							    '''--output_dir'''       ,  type=snake_case__       ,  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()



