



from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block



@dataclass
class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				42







class     _UpperCAmelCase			(       nn.Module     ):




     def __init__(				self ,						a__=3 ,						a__=3 ,						a__=("DownEncoderBlock2D",) ,						a__=(6_4,) ,						a__=2 ,						a__=3_2 ,						a__="silu" ,						a__=True ,						):
         super().__init__()
         A__		 =					layers_per_block

         A__		 =					torch.nn.Convad(
             a__ ,						block_out_channels[0] ,						kernel_size=3 ,						stride=1 ,						padding=1 ,						)

         A__		 =					None
         A__		 =					nn.ModuleList([])

         # down
         A__		 =					block_out_channels[0]
         for i, down_block_type in enumerate(a__):
             A__		 =					output_channel
             A__		 =					block_out_channels[i]
             A__		 =					i == len(a__) - 1

             A__		 =					get_down_block(
                 a__ ,						num_layers=self.layers_per_block ,						in_channels=a__ ,						out_channels=a__ ,						add_downsample=not is_final_block ,						resnet_eps=1e-6 ,						downsample_padding=0 ,						resnet_act_fn=a__ ,						resnet_groups=a__ ,						attention_head_dim=a__ ,						temb_channels=a__ ,						)
             self.down_blocks.append(a__)

         # mid
         A__		 =					UNetMidBlockaD(
             in_channels=block_out_channels[-1] ,						resnet_eps=1e-6 ,						resnet_act_fn=a__ ,						output_scale_factor=1 ,						resnet_time_scale_shift='''default''' ,						attention_head_dim=block_out_channels[-1] ,						resnet_groups=a__ ,						temb_channels=a__ ,						)

         # out
         A__		 =					nn.GroupNorm(num_channels=block_out_channels[-1] ,						num_groups=a__ ,						eps=1e-6)
         A__		 =					nn.SiLU()

         A__		 =					2 * out_channels if double_z else out_channels
         A__		 =					nn.Convad(block_out_channels[-1] ,						a__ ,						3 ,						padding=1)

         A__		 =					False




     def 		snake_case_		(				self ,						a__):
         A__		 =					x
         A__		 =					self.conv_in(a__)

         if self.training and self.gradient_checkpointing:

             def create_custom_forward(a__):
                 def custom_forward(*a__):
                     return module(*a__)

                 return custom_forward

             # down
             if is_torch_version('''>=''' ,						'''1.11.0'''):
                 for down_block in self.down_blocks:
                     A__		 =					torch.utils.checkpoint.checkpoint(
                         create_custom_forward(a__) ,						a__ ,						use_reentrant=a__)
                 # middle
                 A__		 =					torch.utils.checkpoint.checkpoint(
                     create_custom_forward(self.mid_block) ,						a__ ,						use_reentrant=a__)
             else:
                 for down_block in self.down_blocks:
                     A__		 =					torch.utils.checkpoint.checkpoint(create_custom_forward(a__) ,						a__)
                 # middle
                 A__		 =					torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block) ,						a__)

         else:
             # down
             for down_block in self.down_blocks:
                 A__		 =					down_block(a__)

             # middle
             A__		 =					self.mid_block(a__)

         # post-process
         A__		 =					self.conv_norm_out(a__)
         A__		 =					self.conv_act(a__)
         A__		 =					self.conv_out(a__)

         return sample







class     _UpperCAmelCase			(       nn.Module     ):




     def __init__(				self ,						a__=3 ,						a__=3 ,						a__=("UpDecoderBlock2D",) ,						a__=(6_4,) ,						a__=2 ,						a__=3_2 ,						a__="silu" ,						a__="group" ,						):
         super().__init__()
         A__		 =					layers_per_block

         A__		 =					nn.Convad(
             a__ ,						block_out_channels[-1] ,						kernel_size=3 ,						stride=1 ,						padding=1 ,						)

         A__		 =					None
         A__		 =					nn.ModuleList([])

         A__		 =					in_channels if norm_type == '''spatial''' else None

         # mid
         A__		 =					UNetMidBlockaD(
             in_channels=block_out_channels[-1] ,						resnet_eps=1e-6 ,						resnet_act_fn=a__ ,						output_scale_factor=1 ,						resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type ,						attention_head_dim=block_out_channels[-1] ,						resnet_groups=a__ ,						temb_channels=a__ ,						)

         # up
         A__		 =					list(reversed(a__))
         A__		 =					reversed_block_out_channels[0]
         for i, up_block_type in enumerate(a__):
             A__		 =					output_channel
             A__		 =					reversed_block_out_channels[i]

             A__		 =					i == len(a__) - 1

             A__		 =					get_up_block(
                 a__ ,						num_layers=self.layers_per_block + 1 ,						in_channels=a__ ,						out_channels=a__ ,						prev_output_channel=a__ ,						add_upsample=not is_final_block ,						resnet_eps=1e-6 ,						resnet_act_fn=a__ ,						resnet_groups=a__ ,						attention_head_dim=a__ ,						temb_channels=a__ ,						resnet_time_scale_shift=a__ ,						)
             self.up_blocks.append(a__)
             A__		 =					output_channel

         # out
         if norm_type == "spatial":
             A__		 =					SpatialNorm(block_out_channels[0] ,						a__)
         else:
             A__		 =					nn.GroupNorm(num_channels=block_out_channels[0] ,						num_groups=a__ ,						eps=1e-6)
         A__		 =					nn.SiLU()
         A__		 =					nn.Convad(block_out_channels[0] ,						a__ ,						3 ,						padding=1)

         A__		 =					False




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         A__		 =					z
         A__		 =					self.conv_in(a__)

         A__		 =					next(iter(self.up_blocks.parameters())).dtype
         if self.training and self.gradient_checkpointing:

             def create_custom_forward(a__):
                 def custom_forward(*a__):
                     return module(*a__)

                 return custom_forward

             if is_torch_version('''>=''' ,						'''1.11.0'''):
                 # middle
                 A__		 =					torch.utils.checkpoint.checkpoint(
                     create_custom_forward(self.mid_block) ,						a__ ,						a__ ,						use_reentrant=a__)
                 A__		 =					sample.to(a__)

                 # up
                 for up_block in self.up_blocks:
                     A__		 =					torch.utils.checkpoint.checkpoint(
                         create_custom_forward(a__) ,						a__ ,						a__ ,						use_reentrant=a__)
             else:
                 # middle
                 A__		 =					torch.utils.checkpoint.checkpoint(
                     create_custom_forward(self.mid_block) ,						a__ ,						a__)
                 A__		 =					sample.to(a__)

                 # up
                 for up_block in self.up_blocks:
                     A__		 =					torch.utils.checkpoint.checkpoint(create_custom_forward(a__) ,						a__ ,						a__)
         else:
             # middle
             A__		 =					self.mid_block(a__ ,						a__)
             A__		 =					sample.to(a__)

             # up
             for up_block in self.up_blocks:
                 A__		 =					up_block(a__ ,						a__)

        # post-process
         if latent_embeds is None:
             A__		 =					self.conv_norm_out(a__)
         else:
             A__		 =					self.conv_norm_out(a__ ,						a__)
         A__		 =					self.conv_act(a__)
         A__		 =					self.conv_out(a__)

         return sample







class     _UpperCAmelCase			(       nn.Module     ):




     def __init__(				self ,						a__ ,						a__ ,						a__ ,						a__=None ,						a__="random" ,						a__=False ,						a__=True):
         super().__init__()
         A__		 =					n_e
         A__		 =					vq_embed_dim
         A__		 =					beta
         A__		 =					legacy

         A__		 =					nn.Embedding(self.n_e ,						self.vq_embed_dim)
         self.embedding.weight.data.uniform_(-1.0 / self.n_e ,						1.0 / self.n_e)

         A__		 =					remap
         if self.remap is not None:
             self.register_buffer('''used''' ,						torch.tensor(np.load(self.remap)))
             A__		 =					self.used.shape[0]
             A__		 =					unknown_index  # "random" or "extra" or integer
             if self.unknown_index == "extra":
                 A__		 =					self.re_embed
                 A__		 =					self.re_embed + 1
             print(
                 F"Remapping {self.n_e} indices to {self.re_embed} indices. "
                 F"Using {self.unknown_index} for unknown indices.")
         else:
             A__		 =					n_e

         A__		 =					sane_index_shape




     def 		snake_case_		(				self ,						a__):
         A__		 =					inds.shape
         assert len(a__) > 1
         A__		 =					inds.reshape(ishape[0] ,						-1)
         A__		 =					self.used.to(a__)
         A__		 =					(inds[:, :, None] == used[None, None, ...]).long()
         A__		 =					match.argmax(-1)
         A__		 =					match.sum(2) < 1
         if self.unknown_index == "random":
             A__		 =					torch.randint(0 ,						self.re_embed ,						size=new[unknown].shape).to(device=new.device)
         else:
             A__		 =					self.unknown_index
         return new.reshape(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					inds.shape
         assert len(a__) > 1
         A__		 =					inds.reshape(ishape[0] ,						-1)
         A__		 =					self.used.to(a__)
         if self.re_embed > self.used.shape[0]:  # extra token
             A__		 =					0  # simply set to zero
         A__		 =					torch.gather(used[None, :][inds.shape[0] * [0], :] ,						1 ,						a__)
         return back.reshape(a__)




     def 		snake_case_		(				self ,						a__):
         # reshape z -> (batch, height, width, channel) and flatten
         A__		 =					z.permute(0 ,						2 ,						3 ,						1).contiguous()
         A__		 =					z.view(-1 ,						self.vq_embed_dim)

         # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
         A__		 =					torch.argmin(torch.cdist(a__ ,						self.embedding.weight) ,						dim=1)

         A__		 =					self.embedding(a__).view(z.shape)
         A__		 =					None
         A__		 =					None

         # compute loss for embedding
         if not self.legacy:
             A__		 =					self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
         else:
             A__		 =					torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)

         # preserve gradients
         A__		 =					z + (z_q - z).detach()

         # reshape back to match original input shape
         A__		 =					z_q.permute(0 ,						3 ,						1 ,						2).contiguous()

         if self.remap is not None:
             A__		 =					min_encoding_indices.reshape(z.shape[0] ,						-1)  # add batch axis
             A__		 =					self.remap_to_used(a__)
             A__		 =					min_encoding_indices.reshape(-1 ,						1)  # flatten

         if self.sane_index_shape:
             A__		 =					min_encoding_indices.reshape(z_q.shape[0] ,						z_q.shape[2] ,						z_q.shape[3])

         return z_q, loss, (perplexity, min_encodings, min_encoding_indices)




     def 		snake_case_		(				self ,						a__ ,						a__):
         # shape specifying (batch, height, width, channel)
         if self.remap is not None:
             A__		 =					indices.reshape(shape[0] ,						-1)  # add batch axis
             A__		 =					self.unmap_to_all(a__)
             A__		 =					indices.reshape(-1)  # flatten again

         # get quantized latent vectors
         A__		 =					self.embedding(a__)

         if shape is not None:
             A__		 =					z_q.view(a__)
             # reshape back to match original input shape
             A__		 =					z_q.permute(0 ,						3 ,						1 ,						2).contiguous()

         return z_q







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__=False):
         A__		 =					parameters
         A__ ,	A__		 =					torch.chunk(a__ ,						2 ,						dim=1)
         A__		 =					torch.clamp(self.logvar ,						-3_0.0 ,						2_0.0)
         A__		 =					deterministic
         A__		 =					torch.exp(0.5 * self.logvar)
         A__		 =					torch.exp(self.logvar)
         if self.deterministic:
             A__		 =					A__		 =					torch.zeros_like(
                 self.mean ,						device=self.parameters.device ,						dtype=self.parameters.dtype)




     def 		snake_case_		(				self ,						a__ = None):
         # make sure sample is on the same device as the parameters and has same dtype
         A__		 =					randn_tensor(
             self.mean.shape ,						generator=a__ ,						device=self.parameters.device ,						dtype=self.parameters.dtype)
         A__		 =					self.mean + self.std * sample
         return x




     def 		snake_case_		(				self ,						a__=None):
         if self.deterministic:
             return torch.Tensor([0.0])
         else:
             if other is None:
                 return 0.5 * torch.sum(torch.pow(self.mean ,						2) + self.var - 1.0 - self.logvar ,						dim=[1, 2, 3])
             else:
                 return 0.5 * torch.sum(
                     torch.pow(self.mean - other.mean ,						2) / other.var
                     + self.var / other.var
                     - 1.0
                     - self.logvar
                     + other.logvar ,						dim=[1, 2, 3] ,						)




     def 		snake_case_		(				self ,						a__ ,						a__=[1, 2, 3]):
         if self.deterministic:
             return torch.Tensor([0.0])
         A__		 =					np.log(2.0 * np.pi)
         return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean ,						2) / self.var ,						dim=a__)




     def 		snake_case_		(				self):
         return self.mean





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     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




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




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

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					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 		snake_case_		(				self):
         pass




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

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				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''')
