




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class __snake_case			(			__SCREAMING_SNAKE_CASE ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  KandinskyInpaintPipeline
   UpperCAmelCase__  :    Tuple          =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   UpperCAmelCase__  :    Any          =  [
       '''prompt''',
       '''negative_prompt''',
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   UpperCAmelCase__  :    Optional[Any]          =  [
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''negative_prompt''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   UpperCAmelCase__  :    Dict          =  False







   @property
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        return 32







   @property
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        return 32







   @property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        return self.time_input_dim







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self.time_input_dim * 4







   @property
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        return 100







   @property
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
        return tokenizer







   @property
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							MCLIPConfig(
            numDims=self.cross_attention_dim					,						transformerDimensions=self.text_embedder_hidden_size					,						hidden_size=self.text_embedder_hidden_size					,						intermediate_size=37					,						num_attention_heads=4					,						num_hidden_layers=5					,						vocab_size=1005					,						)

        UpperCAmelCase_          =							MultilingualCLIP(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							text_encoder.eval()

        return text_encoder







   @property
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        torch.manual_seed(0)

        UpperCAmelCase_          =							{
            "in_channels": 9,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "text_image",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "text_image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        UpperCAmelCase_          =							UNetaDConditionModel(**_SCREAMING_SNAKE_CASE)
        return model







   @property
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }







   @property
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							VQModel(**self.dummy_movq_kwargs)
        return model







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.dummy_text_encoder
        UpperCAmelCase_          =							self.dummy_tokenizer
        UpperCAmelCase_          =							self.dummy_unet
        UpperCAmelCase_          =							self.dummy_movq

        UpperCAmelCase_          =							DDIMScheduler(
            num_train_timesteps=1000					,						beta_schedule='''linear'''					,						beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						clip_sample=_SCREAMING_SNAKE_CASE					,						set_alpha_to_one=_SCREAMING_SNAKE_CASE					,						steps_offset=1					,						prediction_type='''epsilon'''					,						thresholding=_SCREAMING_SNAKE_CASE					,						)

        UpperCAmelCase_          =							{
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }

        return components







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str=0):

        """simple docstring"""


        UpperCAmelCase_          =							floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1)).to(_SCREAMING_SNAKE_CASE)
        # create init_image
        UpperCAmelCase_          =							floats_tensor((1, 3, 64, 64)					,						rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((256, 256))
        # create mask
        UpperCAmelCase_          =							np.ones((64, 64)					,						dtype=np.floataa)
        UpperCAmelCase_          =							0

        if str(_SCREAMING_SNAKE_CASE).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_SCREAMING_SNAKE_CASE)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							{
            "prompt": "horse",
            "image": init_image,
            "mask_image": mask,
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "generator": generator,
            "height": 64,
            "width": 64,
            "num_inference_steps": 2,
            "guidance_scale": 4.0,
            "output_type": "np",
        }
        return inputs







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							"cpu"

        UpperCAmelCase_          =							self.get_dummy_components()

        UpperCAmelCase_          =							self.pipeline_class(**_SCREAMING_SNAKE_CASE)
        UpperCAmelCase_          =							pipe.to(_SCREAMING_SNAKE_CASE)

        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

        UpperCAmelCase_          =							pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE))
        UpperCAmelCase_          =							output.images

        UpperCAmelCase_          =							pipe(
            **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)					,						return_dict=_SCREAMING_SNAKE_CASE					,						)[0]

        UpperCAmelCase_          =							image[0, -3:, -3:, -1]
        UpperCAmelCase_          =							image_from_tuple[0, -3:, -3:, -1]

        print(F"""image.shape {image.shape}""")

        assert image.shape == (1, 64, 64, 3)

        UpperCAmelCase_          =							np.array(
            [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6])

        assert (
            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
        ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''')

        UpperCAmelCase_          =							load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
        UpperCAmelCase_          =							np.ones((768, 768)					,						dtype=np.floataa)
        UpperCAmelCase_          =							0

        UpperCAmelCase_          =							"a hat"

        UpperCAmelCase_          =							KandinskyPriorPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-1-prior'''					,						torch_dtype=torch.floataa)
        pipe_prior.to(_SCREAMING_SNAKE_CASE)

        UpperCAmelCase_          =							KandinskyInpaintPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-1-inpaint'''					,						torch_dtype=torch.floataa)
        UpperCAmelCase_          =							pipeline.to(_SCREAMING_SNAKE_CASE)
        pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

        UpperCAmelCase_          =							torch.Generator(device='''cpu''').manual_seed(0)
        UpperCAmelCase_          =							pipe_prior(
            _SCREAMING_SNAKE_CASE					,						generator=_SCREAMING_SNAKE_CASE					,						num_inference_steps=5					,						negative_prompt=''''''					,						).to_tuple()

        UpperCAmelCase_          =							pipeline(
            _SCREAMING_SNAKE_CASE					,						image=_SCREAMING_SNAKE_CASE					,						mask_image=_SCREAMING_SNAKE_CASE					,						image_embeds=_SCREAMING_SNAKE_CASE					,						negative_image_embeds=_SCREAMING_SNAKE_CASE					,						generator=_SCREAMING_SNAKE_CASE					,						num_inference_steps=100					,						height=768					,						width=768					,						output_type='''np'''					,						)

        UpperCAmelCase_          =							output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE					,						_SCREAMING_SNAKE_CASE)











"""simple docstring"""






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': 6_5_0, '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': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""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:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""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:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# 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)