from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowerCamelCase_    :		List[Any]							=	logging.get_logger(__name__)  # pylint: disable=invalid-name


class 						a__							(       __SCREAMING_SNAKE_CASE		):



   def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)			->   int:
        super().__init__()

        if hasattr(scheduler.config				,						'steps_offset'   ) and scheduler.config.steps_offset != 1:
             __a		 =       (
                 f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                 f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                 'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                 ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                 ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                 ' file'
             )
             deprecate('steps_offset!=1'				,						'1.0.0'				,						_a				,						standard_warn=_a   )
             __a		 =       dict(scheduler.config   )
             __a		 =       1
             __a		 =       FrozenDict(_a   )

        if hasattr(scheduler.config				,						'skip_prk_steps'   ) and scheduler.config.skip_prk_steps is False:
             __a		 =       (
                 f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                 ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
                 ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
                 ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
                 ' Hub, it would be very nice if you could open a Pull request for the'
                 ' `scheduler/scheduler_config.json` file'
             )
             deprecate('skip_prk_steps not set'				,						'1.0.0'				,						_a				,						standard_warn=_a   )
             __a		 =       dict(scheduler.config   )
             __a		 =       True
             __a		 =       FrozenDict(_a   )

        if safety_checker is None:
             logger.warning(
                 f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                 ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                 ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                 ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                 ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                 ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'   )

        self.register_modules(
            segmentation_model=_a				,						segmentation_processor=_a				,						vae=_a				,						text_encoder=_a				,						tokenizer=_a				,						unet=_a				,						scheduler=_a				,						safety_checker=_a				,						feature_extractor=_a				,						)



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase = "auto"   )			->   Optional[int]:
        if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             __a		 =       self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_a   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
        self.enable_attention_slicing(_a   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('Please install accelerate via `pip install accelerate`'   )

        __a		 =       torch.device('cuda'   )

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
             if cpu_offloaded_model is not None:
                  cpu_offload(_a				,						_a   )



   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
        if self.device != torch.device('meta'   ) or not hasattr(self.unet				,						'_hf_hook'   ):
             return self.device
        for module in self.unet.modules():
             if (
                 hasattr(_a				,						'_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







   @torch.no_grad()
   def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 5_1_2				,						UpperCAmelCase = 5_1_2				,						UpperCAmelCase = 5_0				,						UpperCAmelCase = 7.5				,						UpperCAmelCase = None				,						UpperCAmelCase = 1				,						UpperCAmelCase = 0.0				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = "pil"				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = 1				,						**UpperCAmelCase				,						)			->   Optional[Any]:
        __a		 =       self.segmentation_processor(
            text=[text]				,						images=[image]				,						padding='max_length'				,						return_tensors='pt'   ).to(self.device   )
        __a		 =       self.segmentation_model(**_a   )
        __a		 =       torch.sigmoid(outputs.logits   ).cpu().detach().unsqueeze(-1   ).numpy()
        __a		 =       self.numpy_to_pil(_a   )[0].resize(image.size   )

        # Run inpainting pipeline with the generated mask
        __a		 =       StableDiffusionInpaintPipeline(
            vae=self.vae				,						text_encoder=self.text_encoder				,						tokenizer=self.tokenizer				,						unet=self.unet				,						scheduler=self.scheduler				,						safety_checker=self.safety_checker				,						feature_extractor=self.feature_extractor				,						)
        return inpainting_pipeline(
            prompt=_a				,						image=_a				,						mask_image=_a				,						height=_a				,						width=_a				,						num_inference_steps=_a				,						guidance_scale=_a				,						negative_prompt=_a				,						num_images_per_prompt=_a				,						eta=_a				,						generator=_a				,						latents=_a				,						output_type=_a				,						return_dict=_a				,						callback=_a				,						callback_steps=_a				,						)





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(UpperCAmelCase				,						'log.txt'   )				,						log_print=UpperCAmelCase				,						trace_memory_line_by_line=UpperCAmelCase				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													__a		 =       benchmark.run()
													_check_summary_is_not_empty(result.inference_summary   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'log.txt'   )   ).exists()   )




