






import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Optional[Any]       						=  AudioLDMPipeline
		_snake_case    :    List[Any]       						=  TEXT_TO_AUDIO_PARAMS
		_snake_case    :    Dict       						=  TEXT_TO_AUDIO_BATCH_PARAMS
		_snake_case    :    int       						=  frozenset(
		    [
		        '''num_inference_steps''',
		        '''num_waveforms_per_prompt''',
		        '''generator''',
		        '''latents''',
		        '''output_type''',
		        '''return_dict''',
		        '''callback''',
		        '''callback_steps''',
		    ]			)



		def 					__UpperCAmelCase			(							self   )	->							str:
							torch.manual_seed(0   )
							UpperCAmelCase_    :      Dict            =						UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)				,		layers_per_block=2				,		sample_size=3_2				,		in_channels=4				,		out_channels=4				,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,		cross_attention_dim=(3_2, 6_4)				,		class_embed_type='simple_projection'				,		projection_class_embeddings_input_dim=3_2				,		class_embeddings_concat=_UpperCamelCase				,		)
							UpperCAmelCase_    :      Union[str, Any]            =						DDIMScheduler(
							    beta_start=0.0_00_85				,		beta_end=0.0_12				,		beta_schedule='scaled_linear'				,		clip_sample=_UpperCamelCase				,		set_alpha_to_one=_UpperCamelCase				,		)
							torch.manual_seed(0   )
							UpperCAmelCase_    :      Optional[Any]            =						AutoencoderKL(
							    block_out_channels=[3_2, 6_4]				,		in_channels=1				,		out_channels=1				,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,		latent_channels=4				,		)
							torch.manual_seed(0   )
							UpperCAmelCase_    :      int            =						ClapTextConfig(
							    bos_token_id=0				,		eos_token_id=2				,		hidden_size=3_2				,		intermediate_size=3_7				,		layer_norm_eps=1E-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=1_0_0_0				,		projection_dim=3_2				,		)
							UpperCAmelCase_    :      List[str]            =						ClapTextModelWithProjection(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'				,		model_max_length=7_7   )

							UpperCAmelCase_    :      Any            =						SpeechTaHifiGanConfig(
							    model_in_dim=8				,		sampling_rate=1_6_0_0_0				,		upsample_initial_channel=1_6				,		upsample_rates=[2, 2]				,		upsample_kernel_sizes=[4, 4]				,		resblock_kernel_sizes=[3, 7]				,		resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]				,		normalize_before=_UpperCamelCase				,		)

							UpperCAmelCase_    :      Union[str, Any]            =						SpeechTaHifiGan(_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'vocoder': vocoder,
							}
							return components



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=0   )	->							Optional[int]:
							if str(_UpperCamelCase   ).startswith('mps'   ):
												UpperCAmelCase_    :      Tuple            =						torch.manual_seed(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      List[str]            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						{
							    'prompt': 'A hammer hitting a wooden surface',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							}
							return inputs



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						'cpu'  # ensure determinism for the device-dependent torch.Generator

							UpperCAmelCase_    :      Any            =						self.get_dummy_components()
							UpperCAmelCase_    :      Any            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						audioldm_pipe(**_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						output.audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) == 2_5_6

							UpperCAmelCase_    :      int            =						audio[:1_0]
							UpperCAmelCase_    :      List[str]            =						np.array(
							    [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]   )

							assert np.abs(audio_slice - expected_slice   ).max() < 1E-2



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[int]            =						self.get_dummy_components()
							UpperCAmelCase_    :      Union[str, Any]            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						audioldm_pipe.to(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						3 * [inputs['prompt']]

							# forward
							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe(**_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						output.audios[0]

							UpperCAmelCase_    :      Optional[Any]            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						3 * [inputs.pop('prompt'   )]

							UpperCAmelCase_    :      Union[str, Any]            =						audioldm_pipe.tokenizer(
							    _UpperCamelCase				,		padding='max_length'				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=_UpperCamelCase				,		return_tensors='pt'				,		)
							UpperCAmelCase_    :      str            =						text_inputs['input_ids'].to(_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						audioldm_pipe.text_encoder(
							    _UpperCamelCase				,		)
							UpperCAmelCase_    :      List[Any]            =						prompt_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							UpperCAmelCase_    :      Any            =						F.normalize(_UpperCamelCase				,		dim=-1   )

							UpperCAmelCase_    :      int            =						prompt_embeds

							# forward
							UpperCAmelCase_    :      str            =						audioldm_pipe(**_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						output.audios[0]

							assert np.abs(audio_a - audio_a   ).max() < 1E-2



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Union[str, Any]            =						self.get_dummy_components()
							UpperCAmelCase_    :      Optional[Any]            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						audioldm_pipe.to(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						3 * ['this is a negative prompt']
							UpperCAmelCase_    :      Optional[int]            =						negative_prompt
							UpperCAmelCase_    :      Any            =						3 * [inputs['prompt']]

							# forward
							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe(**_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						output.audios[0]

							UpperCAmelCase_    :      Any            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						3 * [inputs.pop('prompt'   )]

							UpperCAmelCase_    :      List[Any]            =						[]
							for p in [prompt, negative_prompt]:
												UpperCAmelCase_    :      Dict            =						audioldm_pipe.tokenizer(
												    _UpperCamelCase				,		padding='max_length'				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=_UpperCamelCase				,		return_tensors='pt'				,		)
												UpperCAmelCase_    :      Optional[Any]            =						text_inputs['input_ids'].to(_UpperCamelCase   )

												UpperCAmelCase_    :      Optional[int]            =						audioldm_pipe.text_encoder(
												    _UpperCamelCase				,		)
												UpperCAmelCase_    :      List[str]            =						text_embeds.text_embeds
												# additional L_2 normalization over each hidden-state
												UpperCAmelCase_    :      Optional[int]            =						F.normalize(_UpperCamelCase				,		dim=-1   )

												embeds.append(_UpperCamelCase   )

							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						embeds

							# forward
							UpperCAmelCase_    :      Union[str, Any]            =						audioldm_pipe(**_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						output.audios[0]

							assert np.abs(audio_a - audio_a   ).max() < 1E-2



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Union[str, Any]            =						'cpu'  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_    :      Optional[int]            =						self.get_dummy_components()
							UpperCAmelCase_    :      Union[str, Any]            =						PNDMScheduler(skip_prk_steps=_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						'egg cracking'
							UpperCAmelCase_    :      List[Any]            =						audioldm_pipe(**_UpperCamelCase				,		negative_prompt=_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						output.audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) == 2_5_6

							UpperCAmelCase_    :      List[Any]            =						audio[:1_0]
							UpperCAmelCase_    :      int            =						np.array(
							    [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]   )

							assert np.abs(audio_slice - expected_slice   ).max() < 1E-2



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Dict            =						'cpu'  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_    :      Optional[int]            =						self.get_dummy_components()
							UpperCAmelCase_    :      Any            =						PNDMScheduler(skip_prk_steps=_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						'A hammer hitting a wooden surface'

							# test num_waveforms_per_prompt=1 (default)
							UpperCAmelCase_    :      int            =						audioldm_pipe(_UpperCamelCase				,		num_inference_steps=2   ).audios

							assert audios.shape == (1, 2_5_6)

							# test num_waveforms_per_prompt=1 (default) for batch of prompts
							UpperCAmelCase_    :      List[str]            =						2
							UpperCAmelCase_    :      List[str]            =						audioldm_pipe([prompt] * batch_size				,		num_inference_steps=2   ).audios

							assert audios.shape == (batch_size, 2_5_6)

							# test num_waveforms_per_prompt for single prompt
							UpperCAmelCase_    :      List[Any]            =						2
							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe(_UpperCamelCase				,		num_inference_steps=2				,		num_waveforms_per_prompt=_UpperCamelCase   ).audios

							assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

							# test num_waveforms_per_prompt for batch of prompts
							UpperCAmelCase_    :      str            =						2
							UpperCAmelCase_    :      int            =						audioldm_pipe(
							    [prompt] * batch_size				,		num_inference_steps=2				,		num_waveforms_per_prompt=_UpperCamelCase   ).audios

							assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      Union[str, Any]            =						'cpu'  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_    :      Tuple            =						self.get_dummy_components()
							UpperCAmelCase_    :      Optional[int]            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						audioldm_pipe.vocoder.config.sampling_rate

							UpperCAmelCase_    :      str            =						self.get_dummy_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						audioldm_pipe(audio_length_in_s=0.0_16				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						output.audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) / vocoder_sampling_rate == 0.0_16

							UpperCAmelCase_    :      Dict            =						audioldm_pipe(audio_length_in_s=0.0_32				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						output.audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) / vocoder_sampling_rate == 0.0_32



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      Dict            =						self.get_dummy_components()
							UpperCAmelCase_    :      Optional[int]            =						AudioLDMPipeline(**_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						['hey']

							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe(_UpperCamelCase				,		num_inference_steps=1   )
							UpperCAmelCase_    :      Any            =						output.audios.shape
							assert audio_shape == (1, 2_5_6)

							UpperCAmelCase_    :      Any            =						audioldm_pipe.vocoder.config
							config.model_in_dim *= 2
							UpperCAmelCase_    :      List[str]            =						SpeechTaHifiGan(_UpperCamelCase   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						audioldm_pipe(_UpperCamelCase				,		num_inference_steps=1   )
							UpperCAmelCase_    :      str            =						output.audios.shape
							# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
							assert audio_shape == (1, 2_5_6)



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							self._test_inference_batch_single_identical(test_mean_pixel_difference=_UpperCamelCase   )



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,		reason='XFormers attention is only available with CUDA and `xformers` installed'				,		)
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_UpperCamelCase   )





@slow
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase="cpu"				,		_UpperCamelCase=torch.floataa				,		_UpperCamelCase=0   )	->							str:
							UpperCAmelCase_    :      int            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						np.random.RandomState(_UpperCamelCase   ).standard_normal((1, 8, 1_2_8, 1_6)   )
							UpperCAmelCase_    :      List[str]            =						torch.from_numpy(_UpperCamelCase   ).to(device=_UpperCamelCase				,		dtype=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						{
							    'prompt': 'A hammer hitting a wooden surface',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 2.5,
							}
							return inputs



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						AudioLDMPipeline.from_pretrained('cvssp/audioldm'   )
							UpperCAmelCase_    :      List[str]            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						self.get_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						2_5
							UpperCAmelCase_    :      Any            =						audioldm_pipe(**_UpperCamelCase   ).audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) == 8_1_9_2_0

							UpperCAmelCase_    :      Union[str, Any]            =						audio[7_7_2_3_0:7_7_2_4_0]
							UpperCAmelCase_    :      List[Any]            =						np.array(
							    [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]   )
							UpperCAmelCase_    :      Dict            =						np.abs(expected_slice - audio_slice   ).max()
							assert max_diff < 1E-2



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      List[str]            =						AudioLDMPipeline.from_pretrained('cvssp/audioldm'   )
							UpperCAmelCase_    :      str            =						LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config   )
							UpperCAmelCase_    :      Union[str, Any]            =						audioldm_pipe.to(_UpperCamelCase   )
							audioldm_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						self.get_inputs(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						audioldm_pipe(**_UpperCamelCase   ).audios[0]

							assert audio.ndim == 1
							assert len(_UpperCamelCase   ) == 8_1_9_2_0

							UpperCAmelCase_    :      Dict            =						audio[2_7_7_8_0:2_7_7_9_0]
							UpperCAmelCase_    :      Union[str, Any]            =						np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]   )
							UpperCAmelCase_    :      Union[str, Any]            =						np.abs(expected_slice - audio_slice   ).max()
							assert max_diff < 3E-2








import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

 __a :  Tuple						=  Path(lowerCAmelCase__  )
 __a :  Dict						=  save_dir.joinpath(f"rank_{local_rank}_output.json"  )
 torch.cuda.set_device(lowerCAmelCase__  )
 __a :  Dict						=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).cuda()
 if fpaa:
  __a :  str						=  model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCAmelCase__  ,   lowerCAmelCase__  )  # update config with task specific params
 __a :  List[str]						=  generate_kwargs.pop('''num_beams'''  ,   model.config.num_beams  )  # AttributeError risk?
 if num_return_sequences > num_beams:
  __a :  Dict						=  num_return_sequences

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __a :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  str						=  argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''  )
 parser.add_argument('''--data_dir'''  ,   type=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 parser.add_argument('''--task'''  ,   type=lowerCAmelCase__  ,   default='''summarization'''  ,   help='''used for task_specific_params + metrics'''  )
 parser.add_argument('''--bs'''  ,   type=lowerCAmelCase__  ,   default=8  ,   required=lowerCAmelCase__  ,   help='''batch size'''  )
 parser.add_argument(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   help='''will be added to the begininng of src examples'''  )
 parser.add_argument('''--fp16'''  ,   action='''store_true'''  )
 parser.add_argument('''--debug'''  ,   action='''store_true'''  )
 __a :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  list(json_save_dir.glob('''rank_*.json'''  )  )
 if intermediate_files:
  raise ValueError(f"Found files at {json_save_dir} please move or remove them."  )
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __a :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   args.model_name  ,   type_path=args.type_path  ,   bs=args.bs  ,   fpaa=args.fpaa  ,   task=args.task  ,   local_rank=args.local_rank  ,   n_obs=args.n_obs  ,   max_source_length=args.max_source_length  ,   num_return_sequences=args.num_return_sequences  ,   prefix=args.prefix  ,   dataset_kwargs=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

 if args.local_rank <= 0:
  __a :  int						=  Path(args.save_dir  )
  save_dir.mkdir(exist_ok=lowerCAmelCase__  )
  __a :  List[str]						=  gather_results_from_each_node(lowerCAmelCase__  ,   lowerCAmelCase__  ,   args.sync_timeout  )
  __a :  int						=  combine_partial_results(lowerCAmelCase__  )
  if args.num_return_sequences > 1:
   __a :  List[Any]						=  save_dir.joinpath('''pseudolabel_results.json'''  )
   print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"  )
   save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return
  __a :  Any						=  Path(args.data_dir  ).joinpath(args.type_path + '''.target'''  )
  with open(lowerCAmelCase__  ) as f:
   __a :  Optional[int]						=  [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__  )]

  # Calculate metrics, save metrics,  and save _generations.txt
  __a :  str						=  '''translation''' in args.task
  __a :  List[str]						=  calculate_bleu if calc_bleu else calculate_rouge
  __a :  Any						=  '''bleu''' if calc_bleu else '''rouge'''
  __a :  Dict						=  score_fn(lowerCAmelCase__  ,   lowerCAmelCase__  )
  __a :  Dict						=  len(lowerCAmelCase__  )
  __a :  str						=  time.time() - start_time
  __a :  List[str]						=  round(runtime / metrics['''n_obs''']  ,   4  )
  __a :  Tuple						=  num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __a :  Optional[int]						=  save_dir.joinpath(f"{args.type_path}_{metric_name}.json"  )
  save_json(lowerCAmelCase__  ,   lowerCAmelCase__  ,   indent=lowerCAmelCase__  )
  print(lowerCAmelCase__  )
  write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}_generations.txt"  )  )
  if args.debug:
   write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}.target"  )  )
  else:
   shutil.rmtree(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''  )
 # Unreachable


if __name__ == "__main__":
      # Usage for MT:
      run_generate()
