





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     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      List[str]:






       """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=lowercase_,		)
       assert hasattr(self,		'''env'''							)




   def       UpperCamelCase__  (    self,		__magic_name__=1							) ->      Optional[int]:






       """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=lowercase_,		instance_type=self.instance_type,		debugger_hook_config=lowercase_,		hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path},		metric_definitions=self.env.metric_definitions,		py_version='''py36''',		)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       TrainingJobAnalytics(lowercase_							).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCamelCase__							:				Optional[int]          =      TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

       # extract kpis
       UpperCamelCase__							:				str          =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
       UpperCamelCase__							:				List[Any]          =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']							)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCamelCase__							:				Dict          =      (
           Session().describe_training_job(estimator.latest_training_job.name							).get('''TrainingTimeInSeconds''',		999999							)
       )

       # 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},		lowercase_							)




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,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,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            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,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


    def 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()
        A__          =   self.pipeline_class(**lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


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





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


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





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




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







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


    def 						snake_case__     (      self :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1

