



"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__       =	{
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["WhisperTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WhisperForConditionalGeneration",
							    "WhisperModel",
							    "WhisperPreTrainedModel",
							    "WhisperForAudioClassification",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFWhisperForConditionalGeneration",
							    "TFWhisperModel",
							    "TFWhisperPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "FlaxWhisperForConditionalGeneration",
							    "FlaxWhisperModel",
							    "FlaxWhisperPreTrainedModel",
							    "FlaxWhisperForAudioClassification",
							]


if TYPE_CHECKING:
							from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
							from .feature_extraction_whisper import WhisperFeatureExtractor
							from .processing_whisper import WhisperProcessor
							from .tokenization_whisper import WhisperTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_whisper_fast import WhisperTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_whisper import (
														    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WhisperForAudioClassification,
														    WhisperForConditionalGeneration,
														    WhisperModel,
														    WhisperPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_whisper import (
														    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFWhisperForConditionalGeneration,
														    TFWhisperModel,
														    TFWhisperPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_whisper import (
														    FlaxWhisperForAudioClassification,
														    FlaxWhisperForConditionalGeneration,
														    FlaxWhisperModel,
														    FlaxWhisperPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					ShapEImgaImgPipeline
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   List[Any]:



						"""simple docstring"""






						return 8



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size			,		image_size=64			,		projection_dim=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_channels=3			,		num_hidden_layers=5			,		patch_size=1			,		)

						_UpperCAmelCase			=       CLIPVisionModel(A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPImageProcessor(
						    crop_size=2_24			,		do_center_crop=A			,		do_normalize=A			,		do_resize=A			,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,		resample=3			,		size=2_24			,		)

						return image_processor



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'num_attention_heads': 2,
						    'attention_head_dim': 16,
						    'embedding_dim': self.time_input_dim,
						    'num_embeddings': 32,
						    'embedding_proj_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'num_layers': 1,
						    'clip_embed_dim': self.time_input_dim * 2,
						    'additional_embeddings': 0,
						    'time_embed_act_fn': 'gelu',
						    'norm_in_type': 'layer',
						    'embedding_proj_norm_type': 'layer',
						    'encoder_hid_proj_type': None,
						    'added_emb_type': None,
						}

						_UpperCAmelCase			=       PriorTransformer(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'param_shapes': (
						        (self.renderer_dim, 93),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						    ),
						    'd_latent': self.time_input_dim,
						    'd_hidden': self.renderer_dim,
						    'n_output': 12,
						    'background': (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						_UpperCAmelCase			=       ShapERenderer(**A)
						return model



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_prior
						_UpperCAmelCase			=       self.dummy_image_encoder
						_UpperCAmelCase			=       self.dummy_image_processor
						_UpperCAmelCase			=       self.dummy_renderer

						_UpperCAmelCase			=       HeunDiscreteScheduler(
						    beta_schedule='exp'			,		num_train_timesteps=10_24			,		prediction_type='sample'			,		use_karras_sigmas=A			,		clip_sample=A			,		clip_sample_range=1.0			,		)
						_UpperCAmelCase			=       {
						    'prior': prior,
						    'image_encoder': image_encoder,
						    'image_processor': image_processor,
						    'renderer': renderer,
						    'scheduler': scheduler,
						}

						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]			,		A       :  Tuple=0)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 64, 64)			,		rng=random.Random(A)).to(A)

						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    'image': input_image,
						    'generator': generator,
						    'num_inference_steps': 1,
						    'frame_size': 32,
						    'output_type': 'np',
						}
						return inputs



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_UpperCAmelCase			=       output.images[0]
						_UpperCAmelCase			=       image[0, -3:, -3:, -1]

						assert image.shape == (20, 32, 32, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						    ])

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



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						self._test_inference_batch_single_identical(
						    batch_size=2			,		test_max_difference=A			,		relax_max_difference=A			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_dummy_components()
						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       1
						_UpperCAmelCase			=       2

						_UpperCAmelCase			=       self.get_dummy_inputs(A)

						for key in inputs.keys():
									if key in self.batch_params:
												_UpperCAmelCase			=       batch_size * [inputs[key]]

						_UpperCAmelCase			=       pipe(**A			,		num_images_per_prompt=A)[0]

						assert images.shape[0] == batch_size * num_images_per_prompt







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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/shap_e/test_shap_e_img2img_out.npy')
						_UpperCAmelCase			=       ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)

						_UpperCAmelCase			=       pipe(
						    A			,		generator=A			,		guidance_scale=3.0			,		num_inference_steps=64			,		frame_size=64			,		output_type='np'			,		).images[0]

						assert images.shape == (20, 64, 64, 3)

						assert_mean_pixel_difference(A			,		A)
