


'''simple docstring'''




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 			lowerCAmelCase					(					a				):



	def __init__(       self			,       snake_case__			,       snake_case__     ):
		super().__init__()
		self.register_modules(unet=snake_case__			,       scheduler=snake_case__     )



	@torch.no_grad()
	def __call__(       self			,       snake_case__ = 1			,       snake_case__ = None			,       snake_case__ = 50			,       snake_case__ = "pil"			,       snake_case__ = True			,       **snake_case__			,       ):
		lowerCAmelCase       :     str   				=			torch.randn(
		    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,       generator=snake_case__			,       )
		lowerCAmelCase       :     Optional[Any]   				=			image.to(self.device     )

		# set step values
		self.scheduler.set_timesteps(snake_case__     )

		for t in self.progress_bar(self.scheduler.timesteps     ):
			# 1. predict noise model_output
			lowerCAmelCase       :     List[Any]   				=			self.unet(snake_case__			,       snake_case__     ).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			lowerCAmelCase       :     List[str]   				=			self.scheduler.step(snake_case__			,       snake_case__			,       snake_case__     ).prev_sample

		lowerCAmelCase       :     Any   				=			(image / 2 + 0.5).clamp(0			,       1     )
		lowerCAmelCase       :     Any   				=			image.cpu().permute(0			,       2			,       3			,       1     ).numpy()
		if output_type == "pil":
			lowerCAmelCase       :     str   				=			self.numpy_to_pil(snake_case__     )

		if not return_dict:
			return (image,), "This is a local test"

		return ImagePipelineOutput(images=snake_case__     ), "This is a local test"




'''simple docstring'''




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_lowerCAmelCase	:		Optional[int]		  =						importlib.metadata.version('numpy')
_lowerCAmelCase	:		List[Any]		  =						'.'.join([str(x) for x in sys.version_info[:3]])



class 			lowerCAmelCase					(					a				):



	def 					lowercase  (       self     ):
		# lt + different version strings
		require_version_core('numpy<1000.4.5'     )
		require_version_core('numpy<1000.4'     )
		require_version_core('numpy<1000'     )

		# le
		require_version_core('numpy<=1000.4.5'     )
		require_version_core(f"numpy<={numpy_ver}"     )

		# eq
		require_version_core(f"numpy=={numpy_ver}"     )

		# ne
		require_version_core('numpy!=1000.4.5'     )

		# ge
		require_version_core('numpy>=1.0'     )
		require_version_core('numpy>=1.0.0'     )
		require_version_core(f"numpy>={numpy_ver}"     )

		# gt
		require_version_core('numpy>1.0.0'     )

		# mix
		require_version_core('numpy>1.0.0,<1000'     )

		# requirement w/o version
		require_version_core('numpy'     )

		# unmet requirements due to version conflict
		for req in ["numpy==1.0.0", "numpy>=1000.0.0", f"numpy<{numpy_ver}"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn('but found'			,       str(snake_case__     )     )

        # unmet requirements due to missing module
		for req in ["numpipypie>1", "numpipypie2"]:
			try:
				require_version_core(snake_case__     )
			except importlib.metadata.PackageNotFoundError as e:
				self.assertIn(f"The '{req}' distribution was not found and is required by this application"			,       str(snake_case__     )     )
				self.assertIn('Try: pip install transformers -U'			,       str(snake_case__     )     )

        # bogus requirements formats:
        # 1. whole thing
		for req in ["numpy??1.0.0", "numpy1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('requirement needs to be in the pip package format'			,       str(snake_case__     )     )
        # 2. only operators
		for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ValueError as e:
				self.assertIn('need one of '			,       str(snake_case__     )     )



	def 					lowercase  (       self     ):
		# matching requirement
		require_version('python>=3.6.0'     )

		# not matching requirements
		for req in ["python>9.9.9", "python<3.0.0"]:
			try:
				require_version_core(snake_case__     )
			except ImportError as e:
				self.assertIn(f"{req} is required"			,       str(snake_case__     )     )
				self.assertIn(f"but found python=={python_ver}"			,       str(snake_case__     )     )
