



'''simple docstring'''





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowercase_									=    logging.get_logger(__name__)

# General docstring
lowercase_									=    "RegNetConfig"

# Base docstring
lowercase_									=    "facebook/regnet-y-040"
lowercase_									=    [1, 1_088, 7, 7]

# Image classification docstring
lowercase_									=    "facebook/regnet-y-040"
lowercase_									=    "tabby, tabby cat"

lowercase_									=    [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A = 3					,  A = 1					,  A = 1					,  A = "relu"					,  **A					,  )       ->	List[Any]:


      """simple docstring"""



      super().__init__(**A			)
      # The padding and conv has been verified in
      # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
      _a           =    tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2			)
      _a           =    tf.keras.layers.ConvaD(
          filters=A					,  kernel_size=A					,  strides=A					,  padding='''VALID'''					,  groups=A					,  use_bias=A					,  name='''convolution'''					,  )
      _a           =    tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name='''normalization'''			)
      _a           =    ACTaFN[activation] if activation is not None else tf.identity



 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.convolution(self.padding(A			)			)
      _a           =    self.normalization(A			)
      _a           =    self.activation(A			)
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  **A			)       ->	Tuple:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    config.num_channels
      _a           =    TFRegNetConvLayer(
          out_channels=config.embedding_size					,  kernel_size=3					,  stride=2					,  activation=config.hidden_act					,  name='''embedder'''					,  )



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    shape_list(A			)[1]
      if tf.executing_eagerly() and num_channels != self.num_channels:
           raise ValueError(
               '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''			)

      # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
      # So change the input format from `NCHW` to `NHWC`.
      # shape = (batch_size, in_height, in_width, in_channels=num_channels)
      _a           =    tf.transpose(A					,  perm=(0, 2, 3, 1)			)
      _a           =    self.embedder(A			)
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A = 2					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    tf.keras.layers.ConvaD(
          filters=A					,  kernel_size=1					,  strides=A					,  use_bias=A					,  name='''convolution'''			)
      _a           =    tf.keras.layers.BatchNormalization(epsilon=1E-5					,  momentum=0.9					,  name='''normalization'''			)



 def    a__    (self					,  A					,  A = False			)       ->	tf.Tensor:


      """simple docstring"""



      return self.normalization(self.convolution(A			)					,  training=A			)






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  **A			)       ->	List[str]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    tf.keras.layers.GlobalAveragePoolingaD(keepdims=A					,  name='''pooler'''			)
      _a           =    [
          tf.keras.layers.ConvaD(filters=A					,  kernel_size=1					,  activation='''relu'''					,  name='''attention.0'''			),
          tf.keras.layers.ConvaD(filters=A					,  kernel_size=1					,  activation='''sigmoid'''					,  name='''attention.2'''			),
      ]



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    self.pooler(A			)
      for layer_module in self.attention:
           _a           =    layer_module(A			)
      _a           =    hidden_state * pooled
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 1					,  **A			)       ->	Any:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    in_channels != out_channels or stride != 1
      _a           =    max(1					,  out_channels // config.groups_width			)
      _a           =    (
          TFRegNetShortCut(A					,  stride=A					,  name='''shortcut'''			)
          if should_apply_shortcut
          else tf.keras.layers.Activation('''linear'''					,  name='''shortcut'''			)
      )
      # `self.layers` instead of `self.layer` because that is a reserved argument.
      _a           =    [
          TFRegNetConvLayer(A					,  kernel_size=1					,  activation=config.hidden_act					,  name='''layer.0'''			),
          TFRegNetConvLayer(
              A					,  stride=A					,  groups=A					,  activation=config.hidden_act					,  name='''layer.1'''			),
          TFRegNetConvLayer(A					,  kernel_size=1					,  activation=A					,  name='''layer.2'''			),
      ]
      _a           =    ACTaFN[config.hidden_act]



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    hidden_state
      for layer_module in self.layers:
           _a           =    layer_module(A			)
      _a           =    self.shortcut(A			)
      hidden_state += residual
      _a           =    self.activation(A			)
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 1					,  **A			)       ->	Optional[int]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    in_channels != out_channels or stride != 1
      _a           =    max(1					,  out_channels // config.groups_width			)
      _a           =    (
          TFRegNetShortCut(A					,  stride=A					,  name='''shortcut'''			)
          if should_apply_shortcut
          else tf.keras.layers.Activation('''linear'''					,  name='''shortcut'''			)
      )
      _a           =    [
          TFRegNetConvLayer(A					,  kernel_size=1					,  activation=config.hidden_act					,  name='''layer.0'''			),
          TFRegNetConvLayer(
              A					,  stride=A					,  groups=A					,  activation=config.hidden_act					,  name='''layer.1'''			),
          TFRegNetSELayer(A					,  reduced_channels=int(round(in_channels / 4			)			)					,  name='''layer.2'''			),
          TFRegNetConvLayer(A					,  kernel_size=1					,  activation=A					,  name='''layer.3'''			),
      ]
      _a           =    ACTaFN[config.hidden_act]



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    hidden_state
      for layer_module in self.layers:
           _a           =    layer_module(A			)
      _a           =    self.shortcut(A			)
      hidden_state += residual
      _a           =    self.activation(A			)
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A = 2					,  A = 2					,  **A			)       ->	str:


      """simple docstring"""



      super().__init__(**A			)

      _a           =    TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
      _a           =    [
          # downsampling is done in the first layer with stride of 2
          layer(A					,  A					,  A					,  stride=A					,  name='''layers.0'''			),
          *[layer(A					,  A					,  A					,  name=f'''layers.{i+1}'''			) for i in range(depth - 1			)],
      ]



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      for layer_module in self.layers:
           _a           =    layer_module(A			)
      return hidden_state






class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 def __init__(self					,  A					,  **A			)       ->	Optional[Any]:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    []
      # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
      self.stages.append(
          TFRegNetStage(
              A					,  config.embedding_size					,  config.hidden_sizes[0]					,  stride=2 if config.downsample_in_first_stage else 1					,  depth=config.depths[0]					,  name='''stages.0'''					,  )			)
      _a           =    zip(config.hidden_sizes					,  config.hidden_sizes[1:]			)
      for i, ((in_channels, out_channels), depth) in enumerate(zip(A					,  config.depths[1:]			)			):
           self.stages.append(TFRegNetStage(A					,  A					,  A					,  depth=A					,  name=f'''stages.{i+1}'''			)			)



 def    a__    (self					,  A					,  A = False					,  A = True			)       ->	TFBaseModelOutputWithNoAttention:


      """simple docstring"""



      _a           =    () if output_hidden_states else None

      for stage_module in self.stages:
           if output_hidden_states:
                _a           =    hidden_states + (hidden_state,)

           _a           =    stage_module(A			)

      if output_hidden_states:
           _a           =    hidden_states + (hidden_state,)

      if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None			)

      return TFBaseModelOutputWithNoAttention(last_hidden_state=A					,  hidden_states=A			)






@keras_serializable
class __A     (    tf.keras.layers.Layer		):



 '''simple docstring'''







 __lowerCamelCase       :			Any		=			RegNetConfig



 def __init__(self					,  A					,  **A			)       ->	str:


      """simple docstring"""



      super().__init__(**A			)
      _a           =    config
      _a           =    TFRegNetEmbeddings(A					,  name='''embedder'''			)
      _a           =    TFRegNetEncoder(A					,  name='''encoder'''			)
      _a           =    tf.keras.layers.GlobalAveragePoolingaD(keepdims=A					,  name='''pooler'''			)



 @unpack_inputs
 def    a__    (self					,  A					,  A = None					,  A = None					,  A = False					,  )       ->	TFBaseModelOutputWithPoolingAndNoAttention:


      """simple docstring"""



      _a           =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      _a           =    return_dict if return_dict is not None else self.config.use_return_dict

      _a           =    self.embedder(A					,  training=A			)

      _a           =    self.encoder(
          A					,  output_hidden_states=A					,  return_dict=A					,  training=A			)

      _a           =    encoder_outputs[0]
      _a           =    self.pooler(A			)

      # Change to NCHW output format have uniformity in the modules
      _a           =    tf.transpose(A					,  perm=(0, 3, 1, 2)			)
      _a           =    tf.transpose(A					,  perm=(0, 3, 1, 2)			)

      # Change the other hidden state outputs to NCHW as well
      if output_hidden_states:
           _a           =    tuple([tf.transpose(A					,  perm=(0, 3, 1, 2)			) for h in encoder_outputs[1]]			)

      if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

      return TFBaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=A					,  pooler_output=A					,  hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states					,  )






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			RegNetConfig
 __lowerCamelCase       :			List[str]		=			'regnet'
 __lowerCamelCase       :			Optional[int]		=			'pixel_values'



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


      """simple docstring"""



      return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)					,  dtype=tf.floataa			)}





lowercase_									=    R"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowercase_									=    R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top.'  ,  A  ,  )
class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  *A					,  **A			)       ->	List[str]:


      """simple docstring"""



      super().__init__(A					,  *A					,  **A			)
      _a           =    TFRegNetMainLayer(A					,  name='''regnet'''			)



 @unpack_inputs
 @add_start_docstrings_to_model_forward(A			)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC					,  output_type=A					,  config_class=_CONFIG_FOR_DOC					,  modality='''vision'''					,  expected_output=_EXPECTED_OUTPUT_SHAPE					,  )
 def    a__    (self					,  A					,  A = None					,  A = None					,  A=False					,  )       ->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:


      """simple docstring"""



      _a           =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      _a           =    return_dict if return_dict is not None else self.config.use_return_dict

      _a           =    self.regnet(
          pixel_values=A					,  output_hidden_states=A					,  return_dict=A					,  training=A					,  )
      if not return_dict:
           return (outputs[0],) + outputs[1:]

      return TFBaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=outputs.last_hidden_state					,  pooler_output=outputs.pooler_output					,  hidden_states=outputs.hidden_states					,  )






@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '  ,  A  ,  )
class __A     (    A  ,  A		):



 '''simple docstring'''







 def __init__(self					,  A					,  *A					,  **A			)       ->	int:


      """simple docstring"""



      super().__init__(A					,  *A					,  **A			)
      _a           =    config.num_labels
      _a           =    TFRegNetMainLayer(A					,  name='''regnet'''			)
      # classification head
      _a           =    [
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(config.num_labels					,  name='''classifier.1'''			) if config.num_labels > 0 else tf.identity,
      ]



 @unpack_inputs
 @add_start_docstrings_to_model_forward(A			)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT					,  output_type=A					,  config_class=_CONFIG_FOR_DOC					,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,  )
 def    a__    (self					,  A = None					,  A = None					,  A = None					,  A = None					,  A=False					,  )       ->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:


      """simple docstring"""



      _a           =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      _a           =    return_dict if return_dict is not None else self.config.use_return_dict

      _a           =    self.regnet(
          A					,  output_hidden_states=A					,  return_dict=A					,  training=A			)

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

      _a           =    self.classifier[0](A			)
      _a           =    self.classifier[1](A			)

      _a           =    None if labels is None else self.hf_compute_loss(labels=A					,  logits=A			)

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

      return TFSequenceClassifierOutput(loss=A					,  logits=A					,  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, KandinskyImgaImgPipeline, 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 SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	KandinskyImgaImgPipeline
			__A    	=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
			__A    	=	[
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			]
			__A    	=	[
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			__A    	=	False


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 32


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.time_input_dim * 4


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 1_00


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			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			,					)

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

				return text_encoder


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)

				snake_case_   		=			{
				    'in_channels': 4,
				    # 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,
				}

				snake_case_   		=			UNetaDConditionModel(**__UpperCamelCase				)
				return model


			@property
			def       __lowerCAmelCase			(     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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.dummy_text_encoder
				snake_case_   		=			self.dummy_tokenizer
				snake_case_   		=			self.dummy_unet
				snake_case_   		=			self.dummy_movq

				snake_case_   		=			{
				    'num_train_timesteps': 10_00,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				snake_case_   		=			DDIMScheduler(**__UpperCamelCase				)

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

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(seed + 1				)				).to(__UpperCamelCase				)
				# create init_image
				snake_case_   		=			floats_tensor((1, 3, 64, 64)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)

				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'horse',
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

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

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

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

				snake_case_   		=			np.array(
				    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]				)
				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()}"""







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinsky/kandinsky_img2img_frog.npy'				)

				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
				snake_case_   		=			'A red cartoon frog, 4k'

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

				snake_case_   		=			KandinskyImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-1'			,					torch_dtype=torch.floataa				)
				snake_case_   		=			pipeline.to(__UpperCamelCase				)

				pipeline.set_progress_bar_config(disable=__UpperCamelCase				)

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

				snake_case_   		=			pipeline(
				    __UpperCamelCase			,					image=__UpperCamelCase			,					image_embeds=__UpperCamelCase			,					negative_image_embeds=__UpperCamelCase			,					generator=__UpperCamelCase			,					num_inference_steps=1_00			,					height=7_68			,					width=7_68			,					strength=0.2			,					output_type='np'			,					)

				snake_case_   		=			output.images[0]

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

				assert_mean_pixel_difference(__UpperCamelCase			,					__UpperCamelCase				)
