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_  (     UpperCAmelCase_		,			unittest.TestCase				):






				'''simple docstring'''






				a__			:		Any      			=   ShapEImgaImgPipeline
				a__			:		str      			=   ["""image"""]
				a__			:		Dict      			=   ["""image"""]
				a__			:		Dict      			=   [
				    """num_images_per_prompt""",
				    """num_inference_steps""",
				    """generator""",
				    """latents""",
				    """guidance_scale""",
				    """frame_size""",
				    """output_type""",
				    """return_dict""",
				]
				a__			:		Tuple      			=   False







				@property
				def     UpperCamelCase__     (			self) ->			Tuple:
								return 32







				@property
				def     UpperCamelCase__     (			self) ->			str:
								return 32







				@property
				def     UpperCamelCase__     (			self) ->			Dict:
								return self.time_input_dim * 4







				@property
				def     UpperCamelCase__     (			self) ->			str:
								return 8







				@property
				def     UpperCamelCase__     (			self) ->			List[Any]:
								torch.manual_seed(0)
								__UpperCamelCase  :int    						=	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  :Union[str, Any]    						=	CLIPVisionModel(__lowercase)
								return model







				@property
				def     UpperCamelCase__     (			self) ->			Optional[Any]:
								__UpperCamelCase  :Optional[int]    						=	CLIPImageProcessor(
								    crop_size=224				,				do_center_crop=__lowercase				,				do_normalize=__lowercase				,				do_resize=__lowercase				,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,				resample=3				,				size=224				,				)

								return image_processor







				@property
				def     UpperCamelCase__     (			self) ->			Optional[int]:
								torch.manual_seed(0)

								__UpperCamelCase  :Any    						=	{
								    '''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  :List[str]    						=	PriorTransformer(**__lowercase)
								return model







				@property
				def     UpperCamelCase__     (			self) ->			Union[str, Any]:
								torch.manual_seed(0)

								__UpperCamelCase  :Dict    						=	{
								    '''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  :str    						=	ShapERenderer(**__lowercase)
								return model







				def     UpperCamelCase__     (			self) ->			int:
								__UpperCamelCase  :Optional[int]    						=	self.dummy_prior
								__UpperCamelCase  :Optional[Any]    						=	self.dummy_image_encoder
								__UpperCamelCase  :str    						=	self.dummy_image_processor
								__UpperCamelCase  :List[str]    						=	self.dummy_renderer

								__UpperCamelCase  :Tuple    						=	HeunDiscreteScheduler(
								    beta_schedule='''exp'''				,				num_train_timesteps=1_024				,				prediction_type='''sample'''				,				use_karras_sigmas=__lowercase				,				clip_sample=__lowercase				,				clip_sample_range=1.0				,				)
								__UpperCamelCase  :int    						=	{
								    '''prior''': prior,
								    '''image_encoder''': image_encoder,
								    '''image_processor''': image_processor,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components







				def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0) ->			Optional[Any]:
								__UpperCamelCase  :Any    						=	floats_tensor((1, 3, 64, 64)				,				rng=random.Random(__lowercase)).to(__lowercase)

								if str(__lowercase).startswith('''mps'''):
												__UpperCamelCase  :str    						=	torch.manual_seed(__lowercase)
								else:
												__UpperCamelCase  :Optional[int]    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)
								__UpperCamelCase  :Any    						=	{
								    '''image''': input_image,
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 32,
								    '''output_type''': '''np''',
								}
								return inputs







				def     UpperCamelCase__     (			self) ->			Dict:
								__UpperCamelCase  :Optional[Any]    						=	'''cpu'''

								__UpperCamelCase  :Union[str, Any]    						=	self.get_dummy_components()

								__UpperCamelCase  :Dict    						=	self.pipeline_class(**__lowercase)
								__UpperCamelCase  :Any    						=	pipe.to(__lowercase)

								pipe.set_progress_bar_config(disable=__lowercase)

								__UpperCamelCase  :Dict    						=	pipe(**self.get_dummy_inputs(__lowercase))
								__UpperCamelCase  :Tuple    						=	output.images[0]
								__UpperCamelCase  :Optional[int]    						=	image[0, -3:, -3:, -1]

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

								__UpperCamelCase  :List[Any]    						=	np.array(
								    [
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								    ])

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







				def     UpperCamelCase__     (			self) ->			Dict:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2])







				def     UpperCamelCase__     (			self) ->			List[Any]:
								__UpperCamelCase  :Dict    						=	torch_device == '''cpu'''
								__UpperCamelCase  :Union[str, Any]    						=	True
								self._test_inference_batch_single_identical(
								    batch_size=2				,				test_max_difference=__lowercase				,				relax_max_difference=__lowercase				,				)







				def     UpperCamelCase__     (			self) ->			Any:
								__UpperCamelCase  :int    						=	self.get_dummy_components()
								__UpperCamelCase  :Tuple    						=	self.pipeline_class(**__lowercase)
								__UpperCamelCase  :Union[str, Any]    						=	pipe.to(__lowercase)
								pipe.set_progress_bar_config(disable=__lowercase)

								__UpperCamelCase  :Dict    						=	1
								__UpperCamelCase  :Dict    						=	2

								__UpperCamelCase  :Optional[Any]    						=	self.get_dummy_inputs(__lowercase)

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

								__UpperCamelCase  :Optional[int]    						=	pipe(**__lowercase				,				num_images_per_prompt=__lowercase)[0]

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


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






				'''simple docstring'''






				def     UpperCamelCase__     (			self) ->			Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				def     UpperCamelCase__     (			self) ->			int:
								__UpperCamelCase  :Union[str, Any]    						=	load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''')
								__UpperCamelCase  :List[Any]    						=	load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/shap_e/test_shap_e_img2img_out.npy''')
								__UpperCamelCase  :List[Any]    						=	ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''')
								__UpperCamelCase  :Tuple    						=	pipe.to(__lowercase)
								pipe.set_progress_bar_config(disable=__lowercase)

								__UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device=__lowercase).manual_seed(0)

								__UpperCamelCase  :Union[str, Any]    						=	pipe(
								    __lowercase				,				generator=__lowercase				,				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(__lowercase				,				__lowercase)






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





