


'''simple docstring'''




from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


_a   :   List[Any]												=   logging.get_logger(__name__)


# General docstring
_a   :   Union[str, Any]												=   "MobileNetV1Config"

# Base docstring
_a   :   int												=   "google/mobilenet_v1_1.0_224"
_a   :   Any												=   [1, 1_024, 7, 7]

# Image classification docstring
_a   :   Any												=   "google/mobilenet_v1_1.0_224"
_a   :   Tuple												=   "tabby, tabby cat"


_a   :   Tuple												=   [
    "google/mobilenet_v1_1.0_224",
    "google/mobilenet_v1_0.75_192",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]





def   _a		(lowercase__			:   Union[str, Any]							,   lowercase__			:   Tuple							,   lowercase__			:   Tuple=None						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						{}

     if isinstance(lowercase__							,   lowercase__						):
          __snake_case           =						model.mobilenet_va
     else:
          __snake_case           =						model

     __snake_case           =						'MobilenetV1/Conv2d_0/'
     __snake_case           =						backbone.conv_stem.convolution.weight
     __snake_case           =						backbone.conv_stem.normalization.bias
     __snake_case           =						backbone.conv_stem.normalization.weight
     __snake_case           =						backbone.conv_stem.normalization.running_mean
     __snake_case           =						backbone.conv_stem.normalization.running_var

     for i in range(1_3						):
          __snake_case           =						i + 1
          __snake_case           =						i * 2

          __snake_case           =						backbone.layer[pt_index]
          __snake_case           =						f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
          __snake_case           =						pointer.convolution.weight
          __snake_case           =						pointer.normalization.bias
          __snake_case           =						pointer.normalization.weight
          __snake_case           =						pointer.normalization.running_mean
          __snake_case           =						pointer.normalization.running_var

          __snake_case           =						backbone.layer[pt_index + 1]
          __snake_case           =						f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
          __snake_case           =						pointer.convolution.weight
          __snake_case           =						pointer.normalization.bias
          __snake_case           =						pointer.normalization.weight
          __snake_case           =						pointer.normalization.running_mean
          __snake_case           =						pointer.normalization.running_var

     if isinstance(lowercase__							,   lowercase__						):
          __snake_case           =						'MobilenetV1/Logits/Conv2d_1c_1x1/'
          __snake_case           =						model.classifier.weight
          __snake_case           =						model.classifier.bias

     return tf_to_pt_map





def   _a		(lowercase__			:   List[str]							,   lowercase__			:   Optional[Any]							,   lowercase__			:   Tuple						)			->			str:






     """simple docstring"""
     try:
          import numpy as np
          import tensorflow as tf
     except ImportError:
          logger.error(
              'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
              'https://www.tensorflow.org/install/ for installation instructions.'						)
          raise

     # Load weights from TF model
     __snake_case           =						tf.train.list_variables(lowercase__						)
     __snake_case           =						{}
     for name, shape in init_vars:
          logger.info(f'Loading TF weight {name} with shape {shape}'						)
          __snake_case           =						tf.train.load_variable(lowercase__							,   lowercase__						)
          __snake_case           =						array

     # Build TF to PyTorch weights loading map
     __snake_case           =						_build_tf_to_pytorch_map(lowercase__							,   lowercase__							,   lowercase__						)

     for name, pointer in tf_to_pt_map.items():
          logger.info(f'Importing {name}'						)
          if name not in tf_weights:
               logger.info(f'{name} not in tf pre-trained weights, skipping'						)
               continue

          __snake_case           =						tf_weights[name]

          if "depthwise_weights" in name:
               logger.info('Transposing depthwise'						)
               __snake_case           =						np.transpose(lowercase__							,   (2, 3, 0, 1)						)
          elif "weights" in name:
               logger.info('Transposing'						)
               if len(pointer.shape						) == 2:  # copying into linear layer
                    __snake_case           =						array.squeeze().transpose()
               else:
                    __snake_case           =						np.transpose(lowercase__							,   (3, 2, 0, 1)						)

          if pointer.shape != array.shape:
               raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'						)

          logger.info(f'Initialize PyTorch weight {name} {array.shape}'						)
          __snake_case           =						torch.from_numpy(lowercase__						)

          tf_weights.pop(lowercase__							,   lowercase__						)
          tf_weights.pop(name + '/RMSProp'							,   lowercase__						)
          tf_weights.pop(name + '/RMSProp_1'							,   lowercase__						)
          tf_weights.pop(name + '/ExponentialMovingAverage'							,   lowercase__						)

     logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys()						)}'						)
     return model





def   _a		(lowercase__			:   torch.Tensor							,   lowercase__			:   nn.Convad						)			->			torch.Tensor:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						features.shape[-2:]
     __snake_case      ,       __snake_case           =						conv_layer.stride
     __snake_case      ,       __snake_case           =						conv_layer.kernel_size

     if in_height % stride_height == 0:
          __snake_case           =						max(kernel_height - stride_height							,   0						)
     else:
          __snake_case           =						max(kernel_height - (in_height % stride_height)							,   0						)

     if in_width % stride_width == 0:
          __snake_case           =						max(kernel_width - stride_width							,   0						)
     else:
          __snake_case           =						max(kernel_width - (in_width % stride_width)							,   0						)

     __snake_case           =						pad_along_width // 2
     __snake_case           =						pad_along_width - pad_left
     __snake_case           =						pad_along_height // 2
     __snake_case           =						pad_along_height - pad_top

     __snake_case           =						(pad_left, pad_right, pad_top, pad_bottom)
     return nn.functional.pad(lowercase__							,   lowercase__							,   'constant'							,   0.0						)




class 			_lowercase (	nn.Module							):
   def __init__(						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      MobileNetVaConfig       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[int] = 1       ,   SCREAMING_SNAKE_CASE_       :      Optional[int] = 1       ,   SCREAMING_SNAKE_CASE_       :      bool = False       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = True       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool or str] = True       ,   ) ->							None:
        super().__init__()
        __snake_case           =						config

        if in_channels % groups != 0:
             raise ValueError(f'Input channels ({in_channels}) are not divisible by {groups} groups.'      )
        if out_channels % groups != 0:
             raise ValueError(f'Output channels ({out_channels}) are not divisible by {groups} groups.'      )

        __snake_case           =						0 if config.tf_padding else int((kernel_size - 1) / 2      )

        __snake_case           =						nn.Convad(
            in_channels=SCREAMING_SNAKE_CASE_       ,   out_channels=SCREAMING_SNAKE_CASE_       ,   kernel_size=SCREAMING_SNAKE_CASE_       ,   stride=SCREAMING_SNAKE_CASE_       ,   padding=SCREAMING_SNAKE_CASE_       ,   groups=SCREAMING_SNAKE_CASE_       ,   bias=SCREAMING_SNAKE_CASE_       ,   padding_mode='zeros'       ,   )

        if use_normalization:
             __snake_case           =						nn.BatchNormad(
                 num_features=SCREAMING_SNAKE_CASE_       ,   eps=config.layer_norm_eps       ,   momentum=0.9_9_9_7       ,   affine=SCREAMING_SNAKE_CASE_       ,   track_running_stats=SCREAMING_SNAKE_CASE_       ,   )
        else:
             __snake_case           =						None

        if use_activation:
             if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
                  __snake_case           =						ACTaFN[use_activation]
             elif isinstance(config.hidden_act       ,   SCREAMING_SNAKE_CASE_      ):
                  __snake_case           =						ACTaFN[config.hidden_act]
             else:
                  __snake_case           =						config.hidden_act
        else:
             __snake_case           =						None





   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      torch.Tensor      ) ->							torch.Tensor:
        if self.config.tf_padding:
             __snake_case           =						apply_tf_padding(SCREAMING_SNAKE_CASE_       ,   self.convolution      )
        __snake_case           =						self.convolution(SCREAMING_SNAKE_CASE_      )
        if self.normalization is not None:
             __snake_case           =						self.normalization(SCREAMING_SNAKE_CASE_      )
        if self.activation is not None:
             __snake_case           =						self.activation(SCREAMING_SNAKE_CASE_      )
        return features




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Tuple          =	MobileNetVaConfig
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	load_tf_weights_in_mobilenet_va
   _SCREAMING_SNAKE_CASE  :  Any          =	"mobilenet_v1"
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	"pixel_values"
   _SCREAMING_SNAKE_CASE  :  List[Any]          =	False
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Union[nn.Linear, nn.Convad]      ) ->							None:
        if isinstance(SCREAMING_SNAKE_CASE_       ,   (nn.Linear, nn.Convad)      ):
             module.weight.data.normal_(mean=0.0       ,   std=self.config.initializer_range      )
             if module.bias is not None:
                  module.bias.data.zero_()
        elif isinstance(SCREAMING_SNAKE_CASE_       ,   nn.BatchNormad      ):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0      )




_a   :   Optional[Any]												=   R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_a   :   str												=   R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"




@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."						,					__lowercase						,					)
class 			_lowercase (	__lowercase							):
   def __init__(						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      MobileNetVaConfig       ,   SCREAMING_SNAKE_CASE_       :      bool = True      ) ->							Any:
        super().__init__(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						config

        __snake_case           =						32
        __snake_case           =						max(int(depth * config.depth_multiplier      )       ,   config.min_depth      )

        __snake_case           =						MobileNetVaConvLayer(
            SCREAMING_SNAKE_CASE_       ,   in_channels=config.num_channels       ,   out_channels=SCREAMING_SNAKE_CASE_       ,   kernel_size=3       ,   stride=2       ,   )

        __snake_case           =						[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

        __snake_case           =						nn.ModuleList()
        for i in range(13      ):
             __snake_case           =						out_channels

             if strides[i] == 2 or i == 0:
                  depth *= 2
                  __snake_case           =						max(int(depth * config.depth_multiplier      )       ,   config.min_depth      )

             self.layer.append(
                 MobileNetVaConvLayer(
                     SCREAMING_SNAKE_CASE_       ,   in_channels=SCREAMING_SNAKE_CASE_       ,   out_channels=SCREAMING_SNAKE_CASE_       ,   kernel_size=3       ,   stride=strides[i]       ,   groups=SCREAMING_SNAKE_CASE_       ,   )      )

             self.layer.append(
                 MobileNetVaConvLayer(
                     SCREAMING_SNAKE_CASE_       ,   in_channels=SCREAMING_SNAKE_CASE_       ,   out_channels=SCREAMING_SNAKE_CASE_       ,   kernel_size=1       ,   )      )

        __snake_case           =						nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      int      ) ->							Dict:
        raise NotImplementedError





   @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_      )
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC       ,   output_type=SCREAMING_SNAKE_CASE_       ,   config_class=_CONFIG_FOR_DOC       ,   modality='vision'       ,   expected_output=_EXPECTED_OUTPUT_SHAPE       ,   )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[torch.Tensor] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   ) ->							Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
        __snake_case           =						(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __snake_case           =						return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
             raise ValueError('You have to specify pixel_values'      )

        __snake_case           =						self.conv_stem(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						() if output_hidden_states else None

        for i, layer_module in enumerate(self.layer      ):
             __snake_case           =						layer_module(SCREAMING_SNAKE_CASE_      )

             if output_hidden_states:
                  __snake_case           =						all_hidden_states + (hidden_states,)

        __snake_case           =						hidden_states

        if self.pooler is not None:
             __snake_case           =						torch.flatten(self.pooler(SCREAMING_SNAKE_CASE_      )       ,   start_dim=1      )
        else:
             __snake_case           =						None

        if not return_dict:
             return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None      )

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=SCREAMING_SNAKE_CASE_       ,   pooler_output=SCREAMING_SNAKE_CASE_       ,   hidden_states=SCREAMING_SNAKE_CASE_       ,   )




@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "						,					__lowercase						,					)
class 			_lowercase (	__lowercase							):
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      MobileNetVaConfig      ) ->							None:
        super().__init__(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						config.num_labels
        __snake_case           =						MobileNetVaModel(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						self.mobilenet_va.layer[-1].convolution.out_channels

        # Classifier head
        __snake_case           =						nn.Dropout(config.classifier_dropout_prob       ,   inplace=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						nn.Linear(SCREAMING_SNAKE_CASE_       ,   config.num_labels      ) if config.num_labels > 0 else nn.Identity()

        # Initialize weights and apply final processing
        self.post_init()





   @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_      )
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT       ,   output_type=SCREAMING_SNAKE_CASE_       ,   config_class=_CONFIG_FOR_DOC       ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,   )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Optional[torch.Tensor] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[torch.Tensor] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   ) ->							Union[tuple, ImageClassifierOutputWithNoAttention]:
        __snake_case           =						return_dict if return_dict is not None else self.config.use_return_dict

        __snake_case           =						self.mobilenet_va(SCREAMING_SNAKE_CASE_       ,   output_hidden_states=SCREAMING_SNAKE_CASE_       ,   return_dict=SCREAMING_SNAKE_CASE_      )

        __snake_case           =						outputs.pooler_output if return_dict else outputs[1]

        __snake_case           =						self.classifier(self.dropout(SCREAMING_SNAKE_CASE_      )      )

        __snake_case           =						None
        if labels is not None:
             if self.config.problem_type is None:
                  if self.num_labels == 1:
                       __snake_case           =						'regression'
                  elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                       __snake_case           =						'single_label_classification'
                  else:
                       __snake_case           =						'multi_label_classification'

             if self.config.problem_type == "regression":
                  __snake_case           =						MSELoss()
                  if self.num_labels == 1:
                       __snake_case           =						loss_fct(logits.squeeze()       ,   labels.squeeze()      )
                  else:
                       __snake_case           =						loss_fct(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
             elif self.config.problem_type == "single_label_classification":
                  __snake_case           =						CrossEntropyLoss()
                  __snake_case           =						loss_fct(logits.view(-1       ,   self.num_labels      )       ,   labels.view(-1      )      )
             elif self.config.problem_type == "multi_label_classification":
                  __snake_case           =						BCEWithLogitsLoss()
                  __snake_case           =						loss_fct(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )

        if not return_dict:
             __snake_case           =						(logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(
            loss=SCREAMING_SNAKE_CASE_       ,   logits=SCREAMING_SNAKE_CASE_       ,   hidden_states=outputs.hidden_states       ,   )










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

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """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,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''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      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """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      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

										print(f"image.shape {image.shape}"			)

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

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										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      A__    (		self :Dict			):
										'''simple docstring'''

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




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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




