
"""simple docstring"""

import functools
import operator

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


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       Dict									=				{
    "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 __lowerCAmelCase	(   __snake_case    ):
     lowercase		     =		"unispeech-sat"





     def __init__(			self			,  __UpperCAmelCase=32			,  __UpperCAmelCase=768			,  __UpperCAmelCase=12			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3072			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase="group"			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)			,  __UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)			,  __UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)			,  __UpperCAmelCase=False			,  __UpperCAmelCase=128			,  __UpperCAmelCase=16			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True			,  __UpperCAmelCase=0.0_5			,  __UpperCAmelCase=10			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=10			,  __UpperCAmelCase=0			,  __UpperCAmelCase=320			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=100			,  __UpperCAmelCase=256			,  __UpperCAmelCase=256			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase="mean"			,  __UpperCAmelCase=False			,  __UpperCAmelCase=False			,  __UpperCAmelCase=256			,  __UpperCAmelCase=(512, 512, 512, 512, 1500)			,  __UpperCAmelCase=(5, 3, 3, 1, 1)			,  __UpperCAmelCase=(1, 2, 3, 1, 1)			,  __UpperCAmelCase=512			,  __UpperCAmelCase=0			,  __UpperCAmelCase=1			,  __UpperCAmelCase=2			,  __UpperCAmelCase=504			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCamelCase			,  pad_token_id=__UpperCamelCase			,  bos_token_id=__UpperCamelCase			,  eos_token_id=__UpperCamelCase   )
      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= feat_extract_norm
      __UpperCamelCase       						= feat_extract_activation
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= conv_bias
      __UpperCamelCase       						= num_conv_pos_embeddings
      __UpperCamelCase       						= num_conv_pos_embedding_groups
      __UpperCamelCase       						= len(self.conv_dim   )
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= intermediate_size
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= hidden_dropout
      __UpperCamelCase       						= attention_dropout
      __UpperCamelCase       						= activation_dropout
      __UpperCamelCase       						= feat_proj_dropout
      __UpperCamelCase       						= final_dropout
      __UpperCamelCase       						= layerdrop
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= num_clusters
      __UpperCamelCase       						= do_stable_layer_norm
      __UpperCamelCase       						= 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       						= apply_spec_augment
      __UpperCamelCase       						= mask_time_prob
      __UpperCamelCase       						= mask_time_length
      __UpperCamelCase       						= mask_time_min_masks
      __UpperCamelCase       						= mask_feature_prob
      __UpperCamelCase       						= mask_feature_length
      __UpperCamelCase       						= mask_feature_min_masks

      # parameters for pretraining with codevector quantized representations
      __UpperCamelCase       						= num_codevectors_per_group
      __UpperCamelCase       						= num_codevector_groups
      __UpperCamelCase       						= contrastive_logits_temperature
      __UpperCamelCase       						= feat_quantizer_dropout
      __UpperCamelCase       						= num_negatives
      __UpperCamelCase       						= codevector_dim
      __UpperCamelCase       						= proj_codevector_dim
      __UpperCamelCase       						= diversity_loss_weight

      # ctc loss
      __UpperCamelCase       						= ctc_loss_reduction
      __UpperCamelCase       						= ctc_zero_infinity

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

      # XVector-specific parameters. Feel free to ignore for other classes.
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= list(__UpperCamelCase   )
      __UpperCamelCase       						= xvector_output_dim







     @property
     def 		UpperCAmelCase      (			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 PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1005			,  )

      __UpperCamelCase       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      __UpperCamelCase       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

      __UpperCamelCase       						= self.pipeline_class(**__UpperCAmelCase   )
      __UpperCamelCase       						= pipe.to(__UpperCAmelCase   )

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.images

      __UpperCamelCase       						= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase   )			,  return_dict=__UpperCAmelCase			,  )[0]

      __UpperCamelCase       						= image[0, -3:, -3:, -1]
      __UpperCamelCase       						= image_from_tuple[0, -3:, -3:, -1]

      print(F'image.shape {image.shape}'   )

      assert image.shape == (1, 64, 64, 3)

      __UpperCamelCase       						= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )


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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



