
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = DDIMPipeline
	lowerCAmelCase__         = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	lowerCAmelCase__         = PipelineTesterMixin.required_optional_params - {
	    'num_images_per_prompt',
	    'latents',
	    'callback',
	    'callback_steps',
	}
	lowerCAmelCase__         = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					torch.manual_seed(0 )
					lowerCamelCase_						=			UNetaDModel(
					    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=3						,  out_channels=3						,  down_block_types=("DownBlock2D", "AttnDownBlock2D")						,  up_block_types=("AttnUpBlock2D", "UpBlock2D")						,  )
					lowerCamelCase_						=			DDIMScheduler()
					lowerCamelCase_						=			{"unet": unet, "scheduler": scheduler}
					return components



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=0 )				->       Any:
					if str(lowercase ).startswith("mps" ):
									lowerCamelCase_						=			torch.manual_seed(lowercase )
					else:
									lowerCamelCase_						=			torch.Generator(device=lowercase ).manual_seed(lowercase )
					lowerCamelCase_						=			{
					    "batch_size": 1,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "numpy",
					}
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			"cpu"

					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			self.pipeline_class(**lowercase )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )

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

					self.assertEqual(image.shape						,  (1, 32, 32, 3) )
					lowerCamelCase_						=			np.array(
					    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
					lowerCamelCase_						=			np.abs(image_slice.flatten() - expected_slice ).max()
					self.assertLessEqual(lowercase						,  1e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					super().test_save_load_local(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					super().test_save_load_optional_components(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3 )




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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			"google/ddpm-cifar10-32"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			DDIMScheduler()

					lowerCamelCase_						=			DDIMPipeline(unet=lowercase						,  scheduler=lowercase )
					ddim.to(lowercase )
					ddim.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			ddim(generator=lowercase						,  eta=0.0						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			"google/ddpm-ema-bedroom-256"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			DDIMScheduler.from_pretrained(lowercase )

					lowerCamelCase_						=			DDIMPipeline(unet=lowercase						,  scheduler=lowercase )
					ddpm.to(lowercase )
					ddpm.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			ddpm(generator=lowercase						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 256, 256, 3)
					lowerCamelCase_						=			np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




from typing import TYPE_CHECKING

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


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


