

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor

class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=3					,					lowercase=18					,					lowercase=30					,					lowercase=400					,					lowercase=True					,					lowercase=None					,					lowercase=True					,					lowercase=None					,					)     ->  Any:
							'''simple docstring'''






							A__						=	size if size is not None else {"shortest_edge": 20}
							A__						=	crop_size if crop_size is not None else {"height": 18, "width": 18}
							A__						=	parent
							A__						=	batch_size
							A__						=	num_channels
							A__						=	image_size
							A__						=	min_resolution
							A__						=	max_resolution
							A__						=	do_resize
							A__						=	size
							A__						=	do_center_crop
							A__						=	crop_size


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}

@require_torch
@require_vision
class     a__  (    _UpperCAmelCase							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				MobileNetVaImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	MobileNetVaImageProcessingTester(self				)


			@property
			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_resize"				)				)
							self.assertTrue(hasattr(lowercase					,					"size"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"crop_size"				)				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size					,					{"shortest_edge": 20}				)
							self.assertEqual(image_processor.crop_size					,					{"height": 18, "width": 18}				)

							A__						=	self.image_processing_class.from_dict(self.image_processor_dict					,					size=42					,					crop_size=84				)
							self.assertEqual(image_processor.size					,					{"shortest_edge": 42}				)
							self.assertEqual(image_processor.crop_size					,					{"height": 84, "width": 84}				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					Image.Image				)

							# Test not batched input
							A__						=	image_processing(image_inputs[0]					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)

							# Test batched
							A__						=	image_processing(lowercase					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					numpify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					np.ndarray				)

							# Test not batched input
							A__						=	image_processing(image_inputs[0]					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)

							# Test batched
							A__						=	image_processing(lowercase					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					torchify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					torch.Tensor				)

							# Test not batched input
							A__						=	image_processing(image_inputs[0]					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)

							# Test batched
							A__						=	image_processing(lowercase					,					return_tensors="pt"				).pixel_values
							self.assertEqual(
							    encoded_images.shape					,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)








"""simple docstring"""





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






