




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A		,      _A		,      _A=0.0		,      _A = None		,      _A = "geglu"		,      _A = None		,      _A = False		,      _A = False		,      _A = False		,      _A = False		,      _A = True		,      _A = "layer_norm"		,      _A = False		,      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	only_cross_attention

           __lowerCAmelCase           =	(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
           __lowerCAmelCase           =	(num_embeds_ada_norm is not None) and norm_type == "ada_norm"

           if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
                raise ValueError(
                    f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
                    f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""      )

           # Define 3 blocks. Each block has its own normalization layer.
           # 1. Self-Attn
           if self.use_ada_layer_norm:
                __lowerCAmelCase           =	AdaLayerNorm(_A		,      _A      )
           elif self.use_ada_layer_norm_zero:
                __lowerCAmelCase           =	AdaLayerNormZero(_A		,      _A      )
           else:
                __lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )
           __lowerCAmelCase           =	Attention(
               query_dim=_A		,      heads=_A		,      dim_head=_A		,      dropout=_A		,      bias=_A		,      cross_attention_dim=cross_attention_dim if only_cross_attention else None		,      upcast_attention=_A		,      )

           # 2. Cross-Attn
           if cross_attention_dim is not None or double_self_attention:
                # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
                # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
                # the second cross attention block.
                __lowerCAmelCase           =	(
                    AdaLayerNorm(_A		,      _A      )
                    if self.use_ada_layer_norm
                    else nn.LayerNorm(_A		,      elementwise_affine=_A      )
                )
                __lowerCAmelCase           =	Attention(
                    query_dim=_A		,      cross_attention_dim=cross_attention_dim if not double_self_attention else None		,      heads=_A		,      dim_head=_A		,      dropout=_A		,      bias=_A		,      upcast_attention=_A		,      )  # is self-attn if encoder_hidden_states is none
           else:
                __lowerCAmelCase           =	None
                __lowerCAmelCase           =	None

           # 3. Feed-forward
           __lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )
           __lowerCAmelCase           =	FeedForward(_A		,      dropout=_A		,      activation_fn=_A		,      final_dropout=_A      )

           # let chunk size default to None
           __lowerCAmelCase           =	None
           __lowerCAmelCase           =	0






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	chunk_size
           __lowerCAmelCase           =	dim






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      _A = None		,      ):




           """simple docstring"""



           if self.use_ada_layer_norm:
                __lowerCAmelCase           =	self.norma(_A		,      _A      )
           elif self.use_ada_layer_norm_zero:
                __lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	self.norma(
                    _A		,      _A		,      _A		,      hidden_dtype=hidden_states.dtype      )
           else:
                __lowerCAmelCase           =	self.norma(_A      )

           __lowerCAmelCase           =	cross_attention_kwargs if cross_attention_kwargs is not None else {}

           __lowerCAmelCase           =	self.attna(
               _A		,      encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None		,      attention_mask=_A		,      **_A		,      )
           if self.use_ada_layer_norm_zero:
                __lowerCAmelCase           =	gate_msa.unsqueeze(1      ) * attn_output
           __lowerCAmelCase           =	attn_output + hidden_states

           # 2. Cross-Attention
           if self.attna is not None:
                __lowerCAmelCase           =	(
                    self.norma(_A		,      _A      ) if self.use_ada_layer_norm else self.norma(_A      )
                )

                __lowerCAmelCase           =	self.attna(
                    _A		,      encoder_hidden_states=_A		,      attention_mask=_A		,      **_A		,      )
                __lowerCAmelCase           =	attn_output + hidden_states

           # 3. Feed-forward
           __lowerCAmelCase           =	self.norma(_A      )

           if self.use_ada_layer_norm_zero:
                __lowerCAmelCase           =	norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

           if self._chunk_size is not None:
                # "feed_forward_chunk_size" can be used to save memory
                if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                     raise ValueError(
                         f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""      )

                __lowerCAmelCase           =	norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
                __lowerCAmelCase           =	torch.cat(
                    [self.ff(_A      ) for hid_slice in norm_hidden_states.chunk(_A		,      dim=self._chunk_dim      )]		,      dim=self._chunk_dim		,      )
           else:
                __lowerCAmelCase           =	self.ff(_A      )

           if self.use_ada_layer_norm_zero:
                __lowerCAmelCase           =	gate_mlp.unsqueeze(1      ) * ff_output

           __lowerCAmelCase           =	ff_output + hidden_states

           return hidden_states






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A = None		,      _A = 4		,      _A = 0.0		,      _A = "geglu"		,      _A = False		,      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	int(dim * mult      )
           __lowerCAmelCase           =	dim_out if dim_out is not None else dim

           if activation_fn == "gelu":
                __lowerCAmelCase           =	GELU(_A		,      _A      )
           if activation_fn == "gelu-approximate":
                __lowerCAmelCase           =	GELU(_A		,      _A		,      approximate="tanh"      )
           elif activation_fn == "geglu":
                __lowerCAmelCase           =	GEGLU(_A		,      _A      )
           elif activation_fn == "geglu-approximate":
                __lowerCAmelCase           =	ApproximateGELU(_A		,      _A      )

           __lowerCAmelCase           =	nn.ModuleList([]      )
           # project in
           self.net.append(_A      )
           # project dropout
           self.net.append(nn.Dropout(_A      )      )
           # project out
           self.net.append(nn.Linear(_A		,      _A      )      )
           # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
           if final_dropout:
                self.net.append(nn.Dropout(_A      )      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           for module in self.net:
                __lowerCAmelCase           =	module(_A      )
           return hidden_states






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A		,      _A = "none"      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	nn.Linear(_A		,      _A      )
           __lowerCAmelCase           =	approximate






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           if gate.device.type != "mps":
                return F.gelu(_A		,      approximate=self.approximate      )
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa      )		,      approximate=self.approximate      ).to(dtype=gate.dtype      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.proj(_A      )
           __lowerCAmelCase           =	self.gelu(_A      )
           return hidden_states






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	nn.Linear(_A		,      dim_out * 2      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           if gate.device.type != "mps":
                return F.gelu(_A      )
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase       ,							__lowerCAmelCase           =	self.proj(_A      ).chunk(2		,      dim=-1      )
           return hidden_states * self.gelu(_A      )






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	nn.Linear(_A		,      _A      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.proj(_A      )
           return x * torch.sigmoid(1.7_02 * x      )






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	nn.Embedding(_A		,      _A      )
           __lowerCAmelCase           =	nn.SiLU()
           __lowerCAmelCase           =	nn.Linear(_A		,      embedding_dim * 2      )
           __lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.linear(self.silu(self.emb(_A      )      )      )
           __lowerCAmelCase       ,							__lowerCAmelCase           =	torch.chunk(_A		,      2      )
           __lowerCAmelCase           =	self.norm(_A      ) * (1 + scale) + shift
           return x






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A      ):




           """simple docstring"""



           super().__init__()

           __lowerCAmelCase           =	CombinedTimestepLabelEmbeddings(_A		,      _A      )

           __lowerCAmelCase           =	nn.SiLU()
           __lowerCAmelCase           =	nn.Linear(_A		,      6 * embedding_dim		,      bias=_A      )
           __lowerCAmelCase           =	nn.LayerNorm(_A		,      elementwise_affine=_A		,      eps=1E-6      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A=None      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.linear(self.silu(self.emb(_A		,      _A		,      hidden_dtype=_A      )      )      )
           __lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	emb.chunk(6		,      dim=1      )
           __lowerCAmelCase           =	self.norm(_A      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
           return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class       a__   ( nn.Module				):






      def __init__(       self		,      _A		,      _A		,      _A		,      _A = None		,      _A = 1E-5      ):




           """simple docstring"""



           super().__init__()
           __lowerCAmelCase           =	num_groups
           __lowerCAmelCase           =	eps

           if act_fn is None:
                __lowerCAmelCase           =	None
           else:
                __lowerCAmelCase           =	get_activation(_A      )

           __lowerCAmelCase           =	nn.Linear(_A		,      out_dim * 2      )






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




           """simple docstring"""



           if self.act:
                __lowerCAmelCase           =	self.act(_A      )
           __lowerCAmelCase           =	self.linear(_A      )
           __lowerCAmelCase           =	emb[:, :, None, None]
           __lowerCAmelCase       ,							__lowerCAmelCase           =	emb.chunk(2		,      dim=1      )

           __lowerCAmelCase           =	F.group_norm(_A		,      self.num_groups		,      eps=self.eps      )
           __lowerCAmelCase           =	x * (1 + scale) + shift
           return x










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       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__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()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




