
"""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 	UpperCamelCase      :

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







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


				def 				A   (							self						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      torchaudio.load(lowercase__     )
									SCREAMING_SNAKE_CASE           =      self.resampler(lowercase__     ).squeeze().numpy()
									return speech


				def 				A   (							self						,      lowercase__     )       ->       Optional[int]:
									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE           =      self.speech_file_to_array_fn(item['wav_path']     )
									SCREAMING_SNAKE_CASE           =      self.processor(lowercase__						,      sampling_rate=16000						,      return_tensors='pt'						,      padding=lowercase__     )
									if self.cuda:
														SCREAMING_SNAKE_CASE           =      inputs.to(device='cuda'     )

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

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

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

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

									def get_trellis(lowercase__						,      lowercase__						,      lowercase__=0     ):
														SCREAMING_SNAKE_CASE           =      emission.size(0     )
														SCREAMING_SNAKE_CASE           =      len(lowercase__     )

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

									SCREAMING_SNAKE_CASE           =      get_trellis(lowercase__						,      lowercase__						,      lowercase__     )




									@dataclass
									class 	UpperCamelCase      :

													"""simple docstring"""



													UpperCAmelCase_							: int
													UpperCAmelCase_							: int
													UpperCAmelCase_							: float

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

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

																			# 2. Store the path with frame-wise probability.
																			SCREAMING_SNAKE_CASE           =      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						,      lowercase__     )     )

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

									SCREAMING_SNAKE_CASE           =      backtrack(lowercase__						,      lowercase__						,      lowercase__						,      lowercase__     )




									@dataclass
									class 	UpperCamelCase      :

													"""simple docstring"""



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


													def __repr__(							self     )       ->       List[str]:
																		"""simple docstring"""







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




													@property
													def 				A   (							self     )       ->       Union[str, Any]:
																		"""simple docstring"""







																		return self.end - self.start

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

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




				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       Tuple:
									"""simple docstring"""







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

									# load text file
									SCREAMING_SNAKE_CASE           =      open(lowercase__						,      encoding='utf8'     ).readlines()

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

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

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

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






def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser()

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

					SCREAMING_SNAKE_CASE           =      parser.parse_args()

					SCREAMING_SNAKE_CASE           =      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()


"""simple docstring"""




import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =      states.reshape(1,	-1,	model.config.state_dim     )
					SCREAMING_SNAKE_CASE           =      actions.reshape(1,	-1,	model.config.act_dim     )
					SCREAMING_SNAKE_CASE           =      returns_to_go.reshape(1,	-1,	1     )
					SCREAMING_SNAKE_CASE           =      timesteps.reshape(1,	-1     )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =      states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
										SCREAMING_SNAKE_CASE           =      attention_mask.to(dtype=torch.long,	device=states.device     ).reshape(1,	-1     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),	device=states.device,	),
										        states,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),	device=actions.device,	),
										        actions,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),	device=returns_to_go.device,	),
										        returns_to_go,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),	device=timesteps.device     ),
										        timesteps,
										    ],	dim=1,	).to(dtype=torch.long     )
					else:
										SCREAMING_SNAKE_CASE           =      None

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					return action_preds[0, -1]


# build the environment

snake_case					  =						gym.make('Hopper-v3')
snake_case					  =						env.observation_space.shape[0]
snake_case					  =						env.action_space.shape[0]
snake_case					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case					  =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case					  =						torch.from_numpy(state_mean).to(device=device)
snake_case					  =						torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case					  =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case					  =						model.to(device)
model.eval()

for ep in range(1_0):
	snake_case  ,     snake_case					  =						0, 0
	snake_case					  =						env.reset()
	snake_case					  =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case					  =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros(0, device=device, dtype=torch.floataa)

	snake_case					  =						torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
	for t in range(max_ep_len):
		env.render()
		# add padding
		snake_case					  =						torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
		snake_case					  =						torch.cat([rewards, torch.zeros(1, device=device)])

		snake_case					  =						get_action(
		    model,
		    (states.to(dtype=torch.floataa) - state_mean) / state_std,
		    actions.to(dtype=torch.floataa),
		    rewards.to(dtype=torch.floataa),
		    target_return.to(dtype=torch.floataa),
		    timesteps.to(dtype=torch.long),
		)
		snake_case					  =						action
		snake_case					  =						action.detach().cpu().numpy()

		snake_case  ,     snake_case  ,     snake_case  ,     snake_case					  =						env.step(action)

		snake_case					  =						torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		snake_case					  =						torch.cat([states, cur_state], dim=0)
		snake_case					  =						reward

		snake_case					  =						target_return[0, -1] - (reward / scale)
		snake_case					  =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case					  =						torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

		episode_return += reward
		episode_length += 1

		if done:
			break
