


import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		DanceDiffusionPipeline
  _lowercase									=		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _lowercase									=		PipelineTesterMixin.required_optional_params - {
      'callback',
      'latents',
      'callback_steps',
      'output_type',
      'num_images_per_prompt',
  }
  _lowercase									=		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _lowercase									=		False
  _lowercase									=		False

  def    _UpperCamelCase( self			:  Any       ):
        torch.manual_seed(0       )
        a__	:  Tuple     =   UNetaDModel(
            block_out_channels=(32, 32, 64)				,				extra_in_channels=16				,				sample_size=512				,				sample_rate=16_000				,				in_channels=2				,				out_channels=2				,				flip_sin_to_cos=lowerCamelCase__				,				use_timestep_embedding=lowerCamelCase__				,				time_embedding_type="fourier"				,				mid_block_type="UNetMidBlock1D"				,				down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				)
        a__	:  int     =   IPNDMScheduler()

        a__	:  List[Any]     =   {
            "unet": unet,
            "scheduler": scheduler,
        }
        return components

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Optional[int]=0       ):
        if str(lowerCamelCase__       ).startswith("mps"       ):
              a__	:  List[Any]     =   torch.manual_seed(lowerCamelCase__       )
        else:
              a__	:  List[str]     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
        a__	:  Dict     =   {
            "batch_size": 1,
            "generator": generator,
            "num_inference_steps": 4,
        }
        return inputs

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  str     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__	:  Optional[int]     =   self.get_dummy_components()
        a__	:  Any     =   DanceDiffusionPipeline(**lowerCamelCase__       )
        a__	:  str     =   pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Any     =   self.get_dummy_inputs(lowerCamelCase__       )
        a__	:  Tuple     =   pipe(**lowerCamelCase__       )
        a__	:  str     =   output.audios

        a__	:  int     =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, components["unet"].sample_size)
        a__	:  int     =   np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]       )
        assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1E-2

  @skip_mps
  def    _UpperCamelCase( self			:  Optional[int]       ):
        return super().test_save_load_local()

  @skip_mps
  def    _UpperCamelCase( self			:  Dict       ):
        return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )

  @skip_mps
  def    _UpperCamelCase( self			:  List[Any]       ):
        return super().test_save_load_optional_components()

  @skip_mps
  def    _UpperCamelCase( self			:  Tuple       ):
        return super().test_attention_slicing_forward_pass()

  def    _UpperCamelCase( self			:  str       ):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3       )




@slow
@require_torch_gpu
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Tuple     =   torch_device

        a__	:  Optional[int]     =   DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       )
        a__	:  int     =   pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   torch.manual_seed(0       )
        a__	:  List[Any]     =   pipe(generator=lowerCamelCase__				,				num_inference_steps=100				,				audio_length_in_s=4.096       )
        a__	:  Optional[Any]     =   output.audios

        a__	:  Optional[Any]     =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, pipe.unet.sample_size)
        a__	:  Any     =   np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]       )

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

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[str]     =   torch_device

        a__	:  List[Any]     =   DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"				,				torch_dtype=torch.floataa       )
        a__	:  str     =   pipe.to(lowerCamelCase__       )
        pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[Any]     =   torch.manual_seed(0       )
        a__	:  Optional[int]     =   pipe(generator=lowerCamelCase__				,				num_inference_steps=100				,				audio_length_in_s=4.096       )
        a__	:  int     =   output.audios

        a__	:  List[str]     =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, pipe.unet.sample_size)
        a__	:  List[Any]     =   np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]       )

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





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""  ,				"""False""" ) ) is not True  ,				reason="""Skipping test because should only be run when releasing minor transformers version"""  ,				)
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ] )
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											if self.framework == "pytorch":
																	subprocess.run(
																	    f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()  ,     encoding="utf-8"  ,     check=SCREAMING_SNAKE_CASE  ,     )
											assert hasattr(self  ,     "env"    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Any=1    )      ->						List[str]:




											'''simple docstring'''

											return HuggingFace(
											    entry_point=self.script  ,     source_dir=self.env.test_path  ,     role=self.env.role  ,     image_uri=self.env.image_uri  ,     base_job_name=f'''{self.env.base_job_name}-single'''  ,     instance_count=SCREAMING_SNAKE_CASE  ,     instance_type=self.instance_type  ,     debugger_hook_config=SCREAMING_SNAKE_CASE  ,     hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}  ,     metric_definitions=self.env.metric_definitions  ,     py_version="py36"  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						List[str]:




											'''simple docstring'''

											TrainingJobAnalytics(SCREAMING_SNAKE_CASE    ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											__snake_case				=		TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

											# extract kpis
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]    )
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											__snake_case				=		(
											    Session().describe_training_job(estimator.latest_training_job.name    ).get("TrainingTimeInSeconds"  ,     9_9_9_9_9_9    )
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy    )
											assert all(t <= self.results["eval_loss"] for t in eval_loss    )

											# dump tests result into json file to share in PR
											with open(f'''{estimator.latest_training_job.name}.json'''  ,     "w"    ) as outfile:
																	json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}  ,     SCREAMING_SNAKE_CASE    )





