"""simple docstring"""





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):
      lowerCamelCase__	:					torch.FloatTensor





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	,    ) ->      str:
             super().__init__()
             lowercase__    :	Tuple     					=			layers_per_block

             lowercase__    :	Union[str, Any]     					=			torch.nn.Convad(
                 a	,    block_out_channels[0]	,    kernel_size=3	,    stride=1	,    padding=1	,    )

             lowercase__    :	int     					=			None
             lowercase__    :	str     					=			nn.ModuleList([]							)

             # down
             lowercase__    :	int     					=			block_out_channels[0]
             for i, down_block_type in enumerate(a							):
                    lowercase__    :	List[Any]     					=			output_channel
                    lowercase__    :	Optional[int]     					=			block_out_channels[i]
                    lowercase__    :	Union[str, Any]     					=			i == len(a							) - 1

                    lowercase__    :	Tuple     					=			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
             lowercase__    :	int     					=			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
             lowercase__    :	Dict     					=			nn.GroupNorm(num_channels=block_out_channels[-1]	,    num_groups=a	,    eps=1e-6							)
             lowercase__    :	Union[str, Any]     					=			nn.SiLU()

             lowercase__    :	Dict     					=			2 * out_channels if double_z else out_channels
             lowercase__    :	List[Any]     					=			nn.Convad(block_out_channels[-1]	,    a	,    3	,    padding=1							)

             lowercase__    :	Dict     					=			False


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             lowercase__    :	Dict     					=			x
             lowercase__    :	Optional[Any]     					=			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:
                                  lowercase__    :	int     					=			torch.utils.checkpoint.checkpoint(
                                      create_custom_forward(a							)	,    a	,    use_reentrant=a							)
                           # middle
                           lowercase__    :	int     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    use_reentrant=a							)
                    else:
                           for down_block in self.down_blocks:
                                  lowercase__    :	Optional[int]     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(a							)	,    a							)
                           # middle
                           lowercase__    :	Union[str, Any]     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)	,    a							)

             else:
                    # down
                    for down_block in self.down_blocks:
                           lowercase__    :	int     					=			down_block(a							)

                    # middle
                    lowercase__    :	Optional[int]     					=			self.mid_block(a							)

             # post-process
             lowercase__    :	List[Any]     					=			self.conv_norm_out(a							)
             lowercase__    :	Dict     					=			self.conv_act(a							)
             lowercase__    :	Optional[Any]     					=			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"	,    ) ->      Dict:
             super().__init__()
             lowercase__    :	List[Any]     					=			layers_per_block

             lowercase__    :	Optional[int]     					=			nn.Convad(
                 a	,    block_out_channels[-1]	,    kernel_size=3	,    stride=1	,    padding=1	,    )

             lowercase__    :	Tuple     					=			None
             lowercase__    :	Dict     					=			nn.ModuleList([]							)

             lowercase__    :	int     					=			in_channels if norm_type == 'spatial' else None

             # mid
             lowercase__    :	List[Any]     					=			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
             lowercase__    :	Optional[Any]     					=			list(reversed(a							)							)
             lowercase__    :	Dict     					=			reversed_block_out_channels[0]
             for i, up_block_type in enumerate(a							):
                    lowercase__    :	str     					=			output_channel
                    lowercase__    :	str     					=			reversed_block_out_channels[i]

                    lowercase__    :	Tuple     					=			i == len(a							) - 1

                    lowercase__    :	List[Any]     					=			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							)
                    lowercase__    :	Optional[Any]     					=			output_channel

             # out
             if norm_type == "spatial":
                    lowercase__    :	Optional[Any]     					=			SpatialNorm(block_out_channels[0]	,    a							)
             else:
                    lowercase__    :	Union[str, Any]     					=			nn.GroupNorm(num_channels=block_out_channels[0]	,    num_groups=a	,    eps=1e-6							)
             lowercase__    :	Union[str, Any]     					=			nn.SiLU()
             lowercase__    :	Any     					=			nn.Convad(block_out_channels[0]	,    a	,    3	,    padding=1							)

             lowercase__    :	str     					=			False


      def 	_UpperCAmelCase (     self	,    a	,    a=None							) ->      Dict:
             lowercase__    :	List[str]     					=			z
             lowercase__    :	List[Any]     					=			self.conv_in(a							)

             lowercase__    :	Dict     					=			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
                           lowercase__    :	List[Any]     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    a	,    use_reentrant=a							)
                           lowercase__    :	List[Any]     					=			sample.to(a							)

                           # up
                           for up_block in self.up_blocks:
                                  lowercase__    :	Dict     					=			torch.utils.checkpoint.checkpoint(
                                      create_custom_forward(a							)	,    a	,    a	,    use_reentrant=a							)
                    else:
                           # middle
                           lowercase__    :	str     					=			torch.utils.checkpoint.checkpoint(
                               create_custom_forward(self.mid_block							)	,    a	,    a							)
                           lowercase__    :	Optional[Any]     					=			sample.to(a							)

                           # up
                           for up_block in self.up_blocks:
                                  lowercase__    :	Any     					=			torch.utils.checkpoint.checkpoint(create_custom_forward(a							)	,    a	,    a							)
             else:
                    # middle
                    lowercase__    :	Any     					=			self.mid_block(a	,    a							)
                    lowercase__    :	List[Any]     					=			sample.to(a							)

                    # up
                    for up_block in self.up_blocks:
                           lowercase__    :	Any     					=			up_block(a	,    a							)

        # post-process
             if latent_embeds is None:
                    lowercase__    :	int     					=			self.conv_norm_out(a							)
             else:
                    lowercase__    :	Union[str, Any]     					=			self.conv_norm_out(a	,    a							)
             lowercase__    :	Union[str, Any]     					=			self.conv_act(a							)
             lowercase__    :	Optional[int]     					=			self.conv_out(a							)

             return sample





class   UpperCAmelCase_			(					nn.Module):


      def __init__(     self	,    a	,    a	,    a	,    a=None	,    a="random"	,    a=False	,    a=True							) ->      Optional[Any]:
             super().__init__()
             lowercase__    :	int     					=			n_e
             lowercase__    :	str     					=			vq_embed_dim
             lowercase__    :	Tuple     					=			beta
             lowercase__    :	List[Any]     					=			legacy

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

             lowercase__    :	Optional[int]     					=			remap
             if self.remap is not None:
                    self.register_buffer('used'	,    torch.tensor(np.load(self.remap							)							)							)
                    lowercase__    :	Optional[int]     					=			self.used.shape[0]
                    lowercase__    :	List[Any]     					=			unknown_index  # "random" or "extra" or integer
                    if self.unknown_index == "extra":
                           lowercase__    :	Any     					=			self.re_embed
                           lowercase__    :	List[Any]     					=			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:
                    lowercase__    :	str     					=			n_e

             lowercase__    :	str     					=			sane_index_shape


      def 	_UpperCAmelCase (     self	,    a							) ->      List[Any]:
             lowercase__    :	Dict     					=			inds.shape
             assert len(a							) > 1
             lowercase__    :	List[Any]     					=			inds.reshape(ishape[0]	,    -1							)
             lowercase__    :	Tuple     					=			self.used.to(a							)
             lowercase__    :	str     					=			(inds[:, :, None] == used[None, None, ...]).long()
             lowercase__    :	Dict     					=			match.argmax(-1							)
             lowercase__    :	int     					=			match.sum(2							) < 1
             if self.unknown_index == "random":
                    lowercase__    :	List[Any]     					=			torch.randint(0	,    self.re_embed	,    size=new[unknown].shape							).to(device=new.device							)
             else:
                    lowercase__    :	Optional[Any]     					=			self.unknown_index
             return new.reshape(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Tuple:
             lowercase__    :	List[str]     					=			inds.shape
             assert len(a							) > 1
             lowercase__    :	int     					=			inds.reshape(ishape[0]	,    -1							)
             lowercase__    :	str     					=			self.used.to(a							)
             if self.re_embed > self.used.shape[0]:  # extra token
                    lowercase__    :	Dict     					=			0  # simply set to zero
             lowercase__    :	Any     					=			torch.gather(used[None, :][inds.shape[0] * [0], :]	,    1	,    a							)
             return back.reshape(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      List[str]:
             # reshape z -> (batch, height, width, channel) and flatten
             lowercase__    :	str     					=			z.permute(0	,    2	,    3	,    1							).contiguous()
             lowercase__    :	List[str]     					=			z.view(-1	,    self.vq_embed_dim							)

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

             lowercase__    :	str     					=			self.embedding(a							).view(z.shape							)
             lowercase__    :	Union[str, Any]     					=			None
             lowercase__    :	Tuple     					=			None

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

             # preserve gradients
             lowercase__    :	List[str]     					=			z + (z_q - z).detach()

             # reshape back to match original input shape
             lowercase__    :	List[str]     					=			z_q.permute(0	,    3	,    1	,    2							).contiguous()

             if self.remap is not None:
                    lowercase__    :	Optional[int]     					=			min_encoding_indices.reshape(z.shape[0]	,    -1							)  # add batch axis
                    lowercase__    :	Optional[int]     					=			self.remap_to_used(a							)
                    lowercase__    :	Union[str, Any]     					=			min_encoding_indices.reshape(-1	,    1							)  # flatten

             if self.sane_index_shape:
                    lowercase__    :	Any     					=			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 	_UpperCAmelCase (     self	,    a	,    a							) ->      Any:
             # shape specifying (batch, height, width, channel)
             if self.remap is not None:
                    lowercase__    :	Optional[int]     					=			indices.reshape(shape[0]	,    -1							)  # add batch axis
                    lowercase__    :	Any     					=			self.unmap_to_all(a							)
                    lowercase__    :	Dict     					=			indices.reshape(-1							)  # flatten again

             # get quantized latent vectors
             lowercase__    :	Optional[Any]     					=			self.embedding(a							)

             if shape is not None:
                    lowercase__    :	Dict     					=			z_q.view(a							)
                    # reshape back to match original input shape
                    lowercase__    :	Union[str, Any]     					=			z_q.permute(0	,    3	,    1	,    2							).contiguous()

             return z_q





class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a=False							) ->      Any:
             lowercase__    :	Optional[Any]     					=			parameters
             lowercase__					,   lowercase__    :	List[str]     					=			torch.chunk(a	,    2	,    dim=1							)
             lowercase__    :	Any     					=			torch.clamp(self.logvar	,    -30.0	,    20.0							)
             lowercase__    :	Dict     					=			deterministic
             lowercase__    :	List[str]     					=			torch.exp(0.5 * self.logvar							)
             lowercase__    :	Optional[int]     					=			torch.exp(self.logvar							)
             if self.deterministic:
                    lowercase__    :	int     					=			torch.zeros_like(
                        self.mean	,    device=self.parameters.device	,    dtype=self.parameters.dtype							)


      def 	_UpperCAmelCase (     self	,    a = None							) ->      torch.FloatTensor:
             # make sure sample is on the same device as the parameters and has same dtype
             lowercase__    :	List[str]     					=			randn_tensor(
                 self.mean.shape	,    generator=a	,    device=self.parameters.device	,    dtype=self.parameters.dtype							)
             lowercase__    :	Dict     					=			self.mean + self.std * sample
             return x


      def 	_UpperCAmelCase (     self	,    a=None							) ->      Union[str, Any]:
             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 	_UpperCAmelCase (     self	,    a	,    a=[1, 2, 3]							) ->      str:
             if self.deterministic:
                    return torch.Tensor([0.0]							)
             lowercase__    :	Optional[Any]     					=			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 	_UpperCAmelCase (     self							) ->      int:
             return self.mean


"""simple docstring"""





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 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

