






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor





class        a__      (	unittest.TestCase		):






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							1
				__A    =							3
				__A    =							8
				__A    =							8

				__A    =							torch.rand((batch_size, num_channels, height, width)						)

				return sample






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Any:
				if isinstance(image[0]							, PIL.Image.Image						):
						return np.stack([np.array(lowercase__						) for i in image]							, axis=0						)
				elif isinstance(lowercase__							, torch.Tensor						):
						return image.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				return image






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							self.to_np(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(
						    image_processor.preprocess(lowercase__						)							, output_type=lowercase__							, )
						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)
				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)

						__A    =							self.to_np(lowercase__						)
						__A    =							(input_np * 255).round() if output_type == "pil" else input_np
						assert (
						    np.abs(in_np - out_np						).max() < 1e-6
						), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				__A    =							image_processor.numpy_to_pil(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)
						for i, o in zip(lowercase__							, lowercase__						):
								__A    =							np.array(lowercase__						)
								__A    =							self.to_np(lowercase__						) if output_type == "pil" else (self.to_np(lowercase__						) * 255).round()
								assert (
								    np.abs(in_np - out_np						).max() < 1e-6
								), F"""decoded output does not match input for output_type {output_type}"""






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							input_pt_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				__A    =							input_np_ad.squeeze(0						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_ad						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad						).max() < 1e-6






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				assert np.abs(out_pt_ad - out_pt_list						).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list						).max() < 1e-6








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''OpenAIGPTTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OpenAIGPTDoubleHeadsModel''',
						    '''OpenAIGPTForSequenceClassification''',
						    '''OpenAIGPTLMHeadModel''',
						    '''OpenAIGPTModel''',
						    '''OpenAIGPTPreTrainedModel''',
						    '''load_tf_weights_in_openai_gpt''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFOpenAIGPTDoubleHeadsModel''',
						    '''TFOpenAIGPTForSequenceClassification''',
						    '''TFOpenAIGPTLMHeadModel''',
						    '''TFOpenAIGPTMainLayer''',
						    '''TFOpenAIGPTModel''',
						    '''TFOpenAIGPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
						from .tokenization_openai import OpenAIGPTTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_openai_fast import OpenAIGPTTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_openai import (
												    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OpenAIGPTDoubleHeadsModel,
												    OpenAIGPTForSequenceClassification,
												    OpenAIGPTLMHeadModel,
												    OpenAIGPTModel,
												    OpenAIGPTPreTrainedModel,
												    load_tf_weights_in_openai_gpt,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_openai import (
												    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFOpenAIGPTDoubleHeadsModel,
												    TFOpenAIGPTForSequenceClassification,
												    TFOpenAIGPTLMHeadModel,
												    TFOpenAIGPTMainLayer,
												    TFOpenAIGPTModel,
												    TFOpenAIGPTPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






