






import functools
import operator

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


A_       :Dict       =						logging.get_logger(__name__)

A_       :List[str]       =						{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class 				__A     (			lowercase_ ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[int]			 ="""unispeech-sat"""


				def __init__( self							,  lowerCamelCase__=32							,  lowerCamelCase__=768							,  lowerCamelCase__=12							,  lowerCamelCase__=12							,  lowerCamelCase__=3072							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.0							,  lowerCamelCase__=0.0							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.02							,  lowerCamelCase__=1E-5							,  lowerCamelCase__="group"							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=(512, 512, 512, 512, 512, 512, 512)							,  lowerCamelCase__=(5, 2, 2, 2, 2, 2, 2)							,  lowerCamelCase__=(10, 3, 3, 3, 3, 2, 2)							,  lowerCamelCase__=False							,  lowerCamelCase__=128							,  lowerCamelCase__=16							,  lowerCamelCase__=False							,  lowerCamelCase__=True							,  lowerCamelCase__=0.05							,  lowerCamelCase__=10							,  lowerCamelCase__=2							,  lowerCamelCase__=0.0							,  lowerCamelCase__=10							,  lowerCamelCase__=0							,  lowerCamelCase__=320							,  lowerCamelCase__=2							,  lowerCamelCase__=0.1							,  lowerCamelCase__=100							,  lowerCamelCase__=256							,  lowerCamelCase__=256							,  lowerCamelCase__=0.1							,  lowerCamelCase__="mean"							,  lowerCamelCase__=False							,  lowerCamelCase__=False							,  lowerCamelCase__=256							,  lowerCamelCase__=(512, 512, 512, 512, 1500)							,  lowerCamelCase__=(5, 3, 3, 1, 1)							,  lowerCamelCase__=(1, 2, 3, 1, 1)							,  lowerCamelCase__=512							,  lowerCamelCase__=0							,  lowerCamelCase__=1							,  lowerCamelCase__=2							,  lowerCamelCase__=504							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									super().__init__(**lowerCamelCase__							,  pad_token_id=lowerCamelCase__							,  bos_token_id=lowerCamelCase__							,  eos_token_id=lowerCamelCase__    )
									__UpperCamelCase      :					str       		=hidden_size
									__UpperCamelCase      :					List[Any]       		=feat_extract_norm
									__UpperCamelCase      :					str       		=feat_extract_activation
									__UpperCamelCase      :					int       		=list(lowerCamelCase__    )
									__UpperCamelCase      :					Optional[int]       		=list(lowerCamelCase__    )
									__UpperCamelCase      :					Any       		=list(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		=conv_bias
									__UpperCamelCase      :					List[str]       		=num_conv_pos_embeddings
									__UpperCamelCase      :					Optional[int]       		=num_conv_pos_embedding_groups
									__UpperCamelCase      :					Union[str, Any]       		=len(self.conv_dim    )
									__UpperCamelCase      :					Optional[Any]       		=num_hidden_layers
									__UpperCamelCase      :					Optional[Any]       		=intermediate_size
									__UpperCamelCase      :					Tuple       		=hidden_act
									__UpperCamelCase      :					List[str]       		=num_attention_heads
									__UpperCamelCase      :					Union[str, Any]       		=hidden_dropout
									__UpperCamelCase      :					Tuple       		=attention_dropout
									__UpperCamelCase      :					str       		=activation_dropout
									__UpperCamelCase      :					List[str]       		=feat_proj_dropout
									__UpperCamelCase      :					str       		=final_dropout
									__UpperCamelCase      :					Optional[Any]       		=layerdrop
									__UpperCamelCase      :					Union[str, Any]       		=layer_norm_eps
									__UpperCamelCase      :					int       		=initializer_range
									__UpperCamelCase      :					Any       		=vocab_size
									__UpperCamelCase      :					Optional[int]       		=num_clusters
									__UpperCamelCase      :					List[str]       		=do_stable_layer_norm
									__UpperCamelCase      :					Union[str, Any]       		=use_weighted_layer_sum

									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      :					List[str]       		=apply_spec_augment
									__UpperCamelCase      :					Dict       		=mask_time_prob
									__UpperCamelCase      :					List[Any]       		=mask_time_length
									__UpperCamelCase      :					str       		=mask_time_min_masks
									__UpperCamelCase      :					List[str]       		=mask_feature_prob
									__UpperCamelCase      :					int       		=mask_feature_length
									__UpperCamelCase      :					Union[str, Any]       		=mask_feature_min_masks

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

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

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									__UpperCamelCase      :					Optional[Any]       		=classifier_proj_size

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







				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return functools.reduce(operator.mul							,  self.conv_stride							,  1    )





'''simple docstring'''

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						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






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



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return self.time_input_dim * 4



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return 8



      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= 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   ,						)

             lowercase__      			= CLIPVisionModel(UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= CLIPImageProcessor(
                 crop_size=224   ,						do_center_crop=UpperCamelCase   ,						do_normalize=UpperCamelCase   ,						do_resize=UpperCamelCase   ,						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    :    str ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''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,
             }

             lowercase__      			= PriorTransformer(**UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''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,
                 ),
             }
             lowercase__      			= ShapERenderer(**UpperCamelCase )
             return model



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.dummy_prior
             lowercase__      			= self.dummy_image_encoder
             lowercase__      			= self.dummy_image_processor
             lowercase__      			= self.dummy_renderer

             lowercase__      			= HeunDiscreteScheduler(
                 beta_schedule='''exp'''   ,						num_train_timesteps=1024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase   ,						clip_sample=UpperCamelCase   ,						clip_sample_range=1.0   ,						)
             lowercase__      			= {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''image_processor''': image_processor,
                 '''renderer''': renderer,
                 '''scheduler''': scheduler,
             }

             return components



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str=0 ):

             '''simple docstring'''







             lowercase__      			= floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )

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



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= '''cpu'''

             lowercase__      			= self.get_dummy_components()

             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )

             pipe.set_progress_bar_config(disable=UpperCamelCase )

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

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

             lowercase__      			= 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    :    Optional[Any] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= torch_device == '''cpu'''
             lowercase__      			= True
             self._test_inference_batch_single_identical(
                 batch_size=2   ,						test_max_difference=UpperCamelCase   ,						relax_max_difference=UpperCamelCase   ,						)






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.get_dummy_components()
             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= 1
             lowercase__      			= 2

             lowercase__      			= self.get_dummy_inputs(UpperCamelCase )

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

             lowercase__      			= pipe(**UpperCamelCase   ,						num_images_per_prompt=UpperCamelCase )[0]

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






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





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







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

             lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(0 )

             lowercase__      			= pipe(
                 UpperCamelCase   ,						generator=UpperCamelCase   ,						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(UpperCamelCase   ,						UpperCamelCase )




