

from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =42





class     snake_case_			(				_a			, _a							):


							"""simple docstring"""





							__UpperCAmelCase       =True




							@register_to_config
							def __init__( self					,						_A = 3					,						_A = 3					,						_A = ("DownEncoderBlock2D",)					,						_A = ("UpDecoderBlock2D",)					,						_A = (6_4,)					,						_A = 1					,						_A = "silu"					,						_A = 4					,						_A = 3_2					,						_A = 3_2					,						_A = 0.1_8215					,						):
												super().__init__()

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

												# pass init params to Decoder
												__lowerCAmelCase					=							Decoder(
												    in_channels=_A					,						out_channels=_A					,						up_block_types=_A					,						block_out_channels=_A					,						layers_per_block=_A					,						norm_num_groups=_A					,						act_fn=_A					,						)

												__lowerCAmelCase					=							nn.Convad(2 * latent_channels					,						2 * latent_channels					,						1							)
												__lowerCAmelCase					=							nn.Convad(_A					,						_A					,						1							)

												__lowerCAmelCase					=							False
												__lowerCAmelCase					=							False

												# only relevant if vae tiling is enabled
												__lowerCAmelCase					=							self.config.sample_size
												__lowerCAmelCase					=							(
												    self.config.sample_size[0]
												    if isinstance(self.config.sample_size					,						(list, tuple)							)
												    else self.config.sample_size
												)
												__lowerCAmelCase					=							int(sample_size / (2 ** (len(self.config.block_out_channels							) - 1))							)
												__lowerCAmelCase					=							0.25




							def        A__  ( self					,						_A					,						_A=False							):
												if isinstance(_A					,						(Encoder, Decoder)							):
																	__lowerCAmelCase					=							value




							def        A__  ( self					,						_A = True							):
												__lowerCAmelCase					=							use_tiling




							def        A__  ( self							):
												self.enable_tiling(_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							True




							def        A__  ( self							):
												__lowerCAmelCase					=							False




							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def        A__  ( self							):
												__lowerCAmelCase					=							{}

												def fn_recursive_add_processors(_A					,						_A					,						_A							):
																	if hasattr(_A					,						'set_processor'							):
																						__lowerCAmelCase					=							module.processor

																	for sub_name, child in module.named_children():
																						fn_recursive_add_processors(F"""{name}.{sub_name}"""					,						_A					,						_A							)

																	return processors

												for name, module in self.named_children():
																	fn_recursive_add_processors(_A					,						_A					,						_A							)

												return processors




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							len(self.attn_processors.keys()							)

												if isinstance(_A					,						_A							) and len(_A							) != count:
																	raise ValueError(
																	    F"""A dict of processors was passed, but the number of processors {len(_A							)} does not match the"""
																	    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

												def fn_recursive_attn_processor(_A					,						_A					,						_A							):
																	if hasattr(_A					,						'set_processor'							):
																						if not isinstance(_A					,						_A							):
																											module.set_processor(_A							)
																						else:
																											module.set_processor(processor.pop(F"""{name}.processor"""							)							)

																	for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(F"""{name}.{sub_name}"""					,						_A					,						_A							)

												for name, module in self.named_children():
																	fn_recursive_attn_processor(_A					,						_A					,						_A							)




							def        A__  ( self							):
												self.set_attn_processor(AttnProcessor()							)




							@apply_forward_hook
							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																	return self.tiled_encode(_A					,						return_dict=_A							)

												if self.use_slicing and x.shape[0] > 1:
																	__lowerCAmelCase					=							[self.encoder(_A							) for x_slice in x.split(1							)]
																	__lowerCAmelCase					=							torch.cat(_A							)
												else:
																	__lowerCAmelCase					=							self.encoder(_A							)

												__lowerCAmelCase					=							self.quant_conv(_A							)
												__lowerCAmelCase					=							DiagonalGaussianDistribution(_A							)

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_A							)




							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																	return self.tiled_decode(_A					,						return_dict=_A							)

												__lowerCAmelCase					=							self.post_quant_conv(_A							)
												__lowerCAmelCase					=							self.decoder(_A							)

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)




							@apply_forward_hook
							def        A__  ( self					,						_A					,						_A = True							):
												if self.use_slicing and z.shape[0] > 1:
																	__lowerCAmelCase					=							[self._decode(_A							).sample for z_slice in z.split(1							)]
																	__lowerCAmelCase					=							torch.cat(_A							)
												else:
																	__lowerCAmelCase					=							self._decode(_A							).sample

												if not return_dict:
																	return (decoded,)

												return DecoderOutput(sample=_A							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							min(a.shape[2]					,						b.shape[2]					,						_A							)
												for y in range(_A							):
																	__lowerCAmelCase					=							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
												return b




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							min(a.shape[3]					,						b.shape[3]					,						_A							)
												for x in range(_A							):
																	__lowerCAmelCase					=							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
												return b




							def        A__  ( self					,						_A					,						_A = True							):
												__lowerCAmelCase					=							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
												__lowerCAmelCase					=							int(self.tile_latent_min_size * self.tile_overlap_factor							)
												__lowerCAmelCase					=							self.tile_latent_min_size - blend_extent

												# Split the image into 512x512 tiles and encode them separately.
												__lowerCAmelCase					=							[]
												for i in range(0					,						x.shape[2]					,						_A							):
																	__lowerCAmelCase					=							[]
																	for j in range(0					,						x.shape[3]					,						_A							):
																						__lowerCAmelCase					=							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						__lowerCAmelCase					=							self.encoder(_A							)
																						__lowerCAmelCase					=							self.quant_conv(_A							)
																						row.append(_A							)
																	rows.append(_A							)
												__lowerCAmelCase					=							[]
												for i, row in enumerate(_A							):
																	__lowerCAmelCase					=							[]
																	for j, tile in enumerate(_A							):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																											__lowerCAmelCase					=							self.blend_v(rows[i - 1][j]					,						_A					,						_A							)
																						if j > 0:
																											__lowerCAmelCase					=							self.blend_h(row[j - 1]					,						_A					,						_A							)
																						result_row.append(tile[:, :, :row_limit, :row_limit]							)
																	result_rows.append(torch.cat(_A					,						dim=3							)							)

												__lowerCAmelCase					=							torch.cat(_A					,						dim=2							)
												__lowerCAmelCase					=							DiagonalGaussianDistribution(_A							)

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_A							)




							def        A__  ( self					,						_A					,						_A = True							):
												__lowerCAmelCase					=							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)							)
												__lowerCAmelCase					=							int(self.tile_sample_min_size * self.tile_overlap_factor							)
												__lowerCAmelCase					=							self.tile_sample_min_size - blend_extent

												# Split z into overlapping 64x64 tiles and decode them separately.
												# The tiles have an overlap to avoid seams between tiles.
												__lowerCAmelCase					=							[]
												for i in range(0					,						z.shape[2]					,						_A							):
																	__lowerCAmelCase					=							[]
																	for j in range(0					,						z.shape[3]					,						_A							):
																						__lowerCAmelCase					=							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__lowerCAmelCase					=							self.post_quant_conv(_A							)
																						__lowerCAmelCase					=							self.decoder(_A							)
																						row.append(_A							)
																	rows.append(_A							)
												__lowerCAmelCase					=							[]
												for i, row in enumerate(_A							):
																	__lowerCAmelCase					=							[]
																	for j, tile in enumerate(_A							):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																											__lowerCAmelCase					=							self.blend_v(rows[i - 1][j]					,						_A					,						_A							)
																						if j > 0:
																											__lowerCAmelCase					=							self.blend_h(row[j - 1]					,						_A					,						_A							)
																						result_row.append(tile[:, :, :row_limit, :row_limit]							)
																	result_rows.append(torch.cat(_A					,						dim=3							)							)

												__lowerCAmelCase					=							torch.cat(_A					,						dim=2							)
												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)




							def        A__  ( self					,						_A					,						_A = False					,						_A = True					,						_A = None					,						):
												__lowerCAmelCase					=							sample
												__lowerCAmelCase					=							self.encode(_A							).latent_dist
												if sample_posterior:
																	__lowerCAmelCase					=							posterior.sample(generator=_A							)
												else:
																	__lowerCAmelCase					=							posterior.mode()
												__lowerCAmelCase					=							self.decode(_A							).sample

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_A							)









import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												import datasets

												__lowerCAmelCase					=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,						'image'					,						split='test'							)
												__lowerCAmelCase					=							depth_estimator(
												    [
												        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
												        'http://images.cocodataset.org/val2017/000000039769.jpg',
												        # RGBA
												        dataset[0]['file'],
												        # LA
												        dataset[1]['file'],
												        # L
												        dataset[2]['file'],
												    ]							)
												self.assertEqual(
												    [
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												        {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__lowerCAmelCase					=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
												__lowerCAmelCase					=							hashimage(outputs['depth']							)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)					,						29.304							)
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)					,						2.662							)




							@require_torch
							def        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






