

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


lowerCamelCase_					  =   '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''







class 					__lowerCamelCase							(      __snake_case      ):







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			None:
        super().__init__()

        self.register_modules(
            unet=lowerCamelCase				,    scheduler=lowerCamelCase				,    )

        snake_case_					= None







      def       lowerCAmelCase_       (	self				,    lowerCamelCase=0  )		->			Optional[Any]:
        if is_accelerate_available() and is_accelerate_version(""">="""				,    """0.14.0"""  ):
          from accelerate import cpu_offload
        else:
          raise ImportError("""`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"""  )

        snake_case_					= torch.device(f'''cuda:{gpu_id}'''  )

        if self.device.type != "cpu":
          self.to("""cpu"""				,    silence_dtype_warnings=lowerCamelCase  )
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        for cpu_offloaded_model in [self.unet]:
          cpu_offload(lowerCamelCase				,    lowerCamelCase  )

        if self.safety_checker is not None:
          cpu_offload(self.safety_checker				,    execution_device=lowerCamelCase				,    offload_buffers=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase=0  )		->			List[str]:
        if is_accelerate_available() and is_accelerate_version(""">="""				,    """0.17.0.dev0"""  ):
          from accelerate import cpu_offload_with_hook
        else:
          raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )

        snake_case_					= torch.device(f'''cuda:{gpu_id}'''  )

        if self.device.type != "cpu":
          self.to("""cpu"""				,    silence_dtype_warnings=lowerCamelCase  )
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        snake_case_					= None
        for cpu_offloaded_model in [self.unet]:
          snake_case_		,							snake_case_					= cpu_offload_with_hook(lowerCamelCase				,    lowerCamelCase				,    prev_module_hook=lowerCamelCase  )

        if self.safety_checker is not None:
          snake_case_		,							snake_case_					= cpu_offload_with_hook(self.safety_checker				,    lowerCamelCase				,    prev_module_hook=lowerCamelCase  )

        # We'll offload the last model manually.
        snake_case_					= hook







      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def       lowerCAmelCase_       (	self  )		->			str:
        if not hasattr(self.unet				,    """_hf_hook"""  ):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(lowerCamelCase				,    """_hf_hook"""  )
              and hasattr(module._hf_hook				,    """execution_device"""  )
              and module._hf_hook.execution_device is not None
          ):
            return torch.device(module._hf_hook.execution_device  )
        return self.device







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase=None  )		->			List[Any]:
        snake_case_					= (batch_size, num_channels, height, width)
        if isinstance(lowerCamelCase				,    lowerCamelCase  ) and len(lowerCamelCase  ) != batch_size:
          raise ValueError(
              f'''You have passed a list of generators of length {len(lowerCamelCase  )}, but requested an effective batch'''
              f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

        if latents is None:
          snake_case_					= randn_tensor(lowerCamelCase				,    generator=lowerCamelCase				,    device=lowerCamelCase				,    dtype=lowerCamelCase  )
        else:
          snake_case_					= latents.to(device=lowerCamelCase				,    dtype=lowerCamelCase  )

        # scale the initial noise by the standard deviation required by the scheduler
        snake_case_					= latents * self.scheduler.init_noise_sigma
        return latents







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = "pil"  )		->			List[Any]:
        if output_type not in ["pt", "np", "pil"]:
          raise ValueError(
              f'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''  )

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        snake_case_					= (sample / 2 + 0.5).clamp(0				,    1  )
        if output_type == "pt":
          return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        snake_case_					= sample.cpu().permute(0				,    2				,    3				,    1  ).numpy()
        if output_type == "np":
          return sample

        # Output_type must be 'pil'
        snake_case_					= self.numpy_to_pil(lowerCamelCase  )
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase=None  )		->			Optional[int]:
        if self.unet.config.num_class_embeds is not None:
          if isinstance(lowerCamelCase				,    lowerCamelCase  ):
            snake_case_					= torch.tensor(lowerCamelCase				,    dtype=torch.int  )
          elif isinstance(lowerCamelCase				,    lowerCamelCase  ):
            assert batch_size == 1, "Batch size must be 1 if classes is an int"
            snake_case_					= torch.tensor([class_labels]				,    dtype=torch.int  )
          elif class_labels is None:
            # Randomly generate batch_size class labels
            # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
            snake_case_					= torch.randint(0				,    self.unet.config.num_class_embeds				,    size=(batch_size,)  )
          snake_case_					= class_labels.to(lowerCamelCase  )
        else:
          snake_case_					= None
        return class_labels







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Optional[Any]:
        if num_inference_steps is None and timesteps is None:
          raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""  )

        if num_inference_steps is not None and timesteps is not None:
          logger.warning(
              f'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
              """ `timesteps` will be used over `num_inference_steps`."""  )

        if latents is not None:
          snake_case_					= (batch_size, 3, img_size, img_size)
          if latents.shape != expected_shape:
            raise ValueError(f'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''  )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(lowerCamelCase				,    lowerCamelCase  ) or callback_steps <= 0)
        ):
          raise ValueError(
              f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
              f''' {type(lowerCamelCase  )}.'''  )







      @torch.no_grad()
      @replace_example_docstring(lowerCamelCase  )
      def __call__(	self				,    lowerCamelCase = 1				,    lowerCamelCase = None				,    lowerCamelCase = 1				,    lowerCamelCase = None				,    lowerCamelCase = None				,    lowerCamelCase = None				,    lowerCamelCase = "pil"				,    lowerCamelCase = True				,    lowerCamelCase = None				,    lowerCamelCase = 1				,    )		->			str:
        snake_case_					= self.unet.config.sample_size
        snake_case_					= self._execution_device

        # 1. Check inputs
        self.check_inputs(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        snake_case_					= self.prepare_latents(
            batch_size=lowerCamelCase				,    num_channels=self.unet.config.in_channels				,    height=lowerCamelCase				,    width=lowerCamelCase				,    dtype=self.unet.dtype				,    device=lowerCamelCase				,    generator=lowerCamelCase				,    latents=lowerCamelCase				,    )

        # 3. Handle class_labels for class-conditional models
        snake_case_					= self.prepare_class_labels(lowerCamelCase				,    lowerCamelCase				,    class_labels=lowerCamelCase  )

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=lowerCamelCase				,    device=lowerCamelCase  )
          snake_case_					= self.scheduler.timesteps
          snake_case_					= len(lowerCamelCase  )
        else:
          self.scheduler.set_timesteps(lowerCamelCase  )
          snake_case_					= self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=lowerCamelCase  ) as progress_bar:
          for i, t in enumerate(lowerCamelCase  ):
            snake_case_					= self.scheduler.scale_model_input(lowerCamelCase				,    lowerCamelCase  )
            snake_case_					= self.unet(lowerCamelCase				,    lowerCamelCase				,    class_labels=lowerCamelCase				,    return_dict=lowerCamelCase  )[0]

            snake_case_					= self.scheduler.step(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    generator=lowerCamelCase  )[0]

            # call the callback, if provided
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )

        # 6. Post-process image sample
        snake_case_					= self.postprocess_image(lowerCamelCase				,    output_type=lowerCamelCase  )

        # Offload last model to CPU
        if hasattr(self				,    """final_offload_hook"""  ) and self.final_offload_hook is not None:
          self.final_offload_hook.offload()

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=lowerCamelCase  )


import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


lowerCamelCase_					  =   random.Random()



def   UpperCamelCase(   lowercase_     ,   lowercase_=1.0     ,   lowercase_=None     ,   lowercase_=None				)      ->							List[str]:





  '''simple docstring'''





  if rng is None:
    snake_case_					= global_rng

  snake_case_					= []
  for batch_idx in range(shape[0]				):
    values.append([]				)
    for _ in range(shape[1]				):
      values[-1].append(rng.random() * scale				)

  return values







@require_torch
@require_sentencepiece
class 					__lowerCamelCase							(      unittest.TestCase      ):







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/musicgen-small"""
        snake_case_					= tempfile.mkdtemp()







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			int:
        return TaTokenizer.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Dict:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case_					= MusicgenProcessor.from_pretrained(self.tmpdirname  )

        self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()  )
        self.assertIsInstance(processor.tokenizer				,    lowerCamelCase  )

        self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()  )
        self.assertIsInstance(processor.feature_extractor				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= MusicgenProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

        snake_case_					= self.get_tokenizer(bos_token="""(BOS)"""				,    eos_token="""(EOS)"""  )
        snake_case_					= self.get_feature_extractor(do_normalize=lowerCamelCase				,    padding_value=1.0  )

        snake_case_					= MusicgenProcessor.from_pretrained(
            self.tmpdirname				,    bos_token="""(BOS)"""				,    eos_token="""(EOS)"""				,    do_normalize=lowerCamelCase				,    padding_value=1.0  )

        self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()  )
        self.assertIsInstance(processor.tokenizer				,    lowerCamelCase  )

        self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()  )
        self.assertIsInstance(processor.feature_extractor				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= processor(lowerCamelCase				,    return_tensors="""np"""  )

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]  )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_					= processor.batch_decode(sequences=lowerCamelCase  )
        snake_case_					= tokenizer.batch_decode(lowerCamelCase  )

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        self.assertListEqual(
            processor.model_input_names				,    feature_extractor.model_input_names				,    msg="""`processor` and `feature_extractor` model input names do not match"""				,    )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.get_feature_extractor(padding_side="""left"""  )
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= [floats_list((1, x)  )[0] for x in range(5				,    20				,    5  )]
        snake_case_					= processor(lowerCamelCase  ).padding_mask

        snake_case_					= np.asarray(floats_list((3, 20)  )  )[:, None, :]
        snake_case_					= processor.batch_decode(lowerCamelCase				,    padding_mask=lowerCamelCase  )

        self.assertIsInstance(lowerCamelCase				,    lowerCamelCase  )

        for audio in decoded_audios:
          self.assertIsInstance(lowerCamelCase				,    np.ndarray  )

        self.assertTrue(decoded_audios[0].shape == (1, 10)  )
        self.assertTrue(decoded_audios[1].shape == (1, 15)  )
        self.assertTrue(decoded_audios[2].shape == (1, 20)  )