


'''simple docstring'''
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__    					=      5_0_0_0_0
UpperCamelCase__    					=      5_0_0_0

UpperCamelCase__					,   UpperCamelCase__    					=      os.path.split(__file__)
UpperCamelCase__    					=      os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	str:
    for i in range(lowerCAmelCase__						):
        UpperCAmelCase__			:		Union[str, Any]													=				dataset[i]





@get_duration
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    for i in range(0  ,					len(lowerCAmelCase__						)  ,					lowerCAmelCase__						):
        UpperCAmelCase__			:		Any													=				dataset[i : i + batch_size]





@get_duration
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    with dataset.formatted_as(type=lowerCAmelCase__						):
        for i in range(lowerCAmelCase__						):
            UpperCAmelCase__			:		Union[str, Any]													=				dataset[i]





@get_duration
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[Any]:
    with dataset.formatted_as(type=lowerCAmelCase__						):
        for i in range(0  ,					lowerCAmelCase__  ,					lowerCAmelCase__						):
            UpperCAmelCase__			:		List[Any]													=				dataset[i : i + batch_size]





def 					a__       (					)    ->	int:
    UpperCAmelCase__			:		List[Any]													=				{'''num examples''': SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase__			:		Optional[int]													=				[
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
    ]

    UpperCAmelCase__			:		Union[str, Any]													=				[
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''						)
        UpperCAmelCase__			:		Tuple													=				datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        UpperCAmelCase__			:		int													=				generate_example_dataset(
            os.path.join(lowerCAmelCase__  ,					'''dataset.arrow'''						)  ,					lowerCAmelCase__  ,					num_examples=lowerCAmelCase__  ,					seq_shapes={'''list''': (1_00,)}  ,					)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__  ,					str(lowerCAmelCase__						)						)
            UpperCAmelCase__			:		Dict													=				func(lowerCAmelCase__  ,					**lowerCAmelCase__						)

        print('''shuffling dataset'''						)
        UpperCAmelCase__			:		Dict													=				dataset.shuffle()
        print('''Second set of iterations (after shuffling'''						)
        for func, kwargs in functions_shuffled:
            print('''shuffled '''  ,					func.__name__  ,					str(lowerCAmelCase__						)						)
            UpperCAmelCase__			:		Any													=				func(
                lowerCAmelCase__  ,					**lowerCAmelCase__						)

    with open(lowerCAmelCase__  ,					'''wb'''						) as f:
        f.write(json.dumps(lowerCAmelCase__						).encode('''utf-8'''						)						)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()




'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
