



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class 	__snake_case   ( unittest.TestCase ):
		def __init__(							self    ,a_    ,a_=7    ,a_=3    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=[0.5, 0.5, 0.5]    ,a_=[0.5, 0.5, 0.5]    ,a_=True    ,a_=1 / 255    ,a_=True    ,):







						"""simple docstring"""
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__		  =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  min_resolution
						lowerCAmelCase__		  =  max_resolution
						lowerCAmelCase__		  =  do_resize
						lowerCAmelCase__		  =  size
						lowerCAmelCase__		  =  do_normalize
						lowerCAmelCase__		  =  image_mean
						lowerCAmelCase__		  =  image_std
						lowerCAmelCase__		  =  do_rescale
						lowerCAmelCase__		  =  rescale_factor
						lowerCAmelCase__		  =  do_pad
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						if not batched:
										lowerCAmelCase__		  =  image_inputs[0]
										if isinstance(a_    ,Image.Image						):
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.size
										else:
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[1], image.shape[2]
										if w < h:
														lowerCAmelCase__		  =  int(self.size['shortest_edge'] * h / w						)
														lowerCAmelCase__		  =  self.size['shortest_edge']
										elif w > h:
														lowerCAmelCase__		  =  self.size['shortest_edge']
														lowerCAmelCase__		  =  int(self.size['shortest_edge'] * w / h						)
										else:
														lowerCAmelCase__		  =  self.size['shortest_edge']
														lowerCAmelCase__		  =  self.size['shortest_edge']

						else:
										lowerCAmelCase__		  =  []
										for image in image_inputs:
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.get_expected_values([image]						)
														expected_values.append((expected_height, expected_width)						)
										lowerCAmelCase__		  =  max(a_    ,key=lambda a_						: item[0]						)[0]
										lowerCAmelCase__		  =  max(a_    ,key=lambda a_						: item[1]						)[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	DetaImageProcessor if is_vision_available() else None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  DetaImageProcessingTester(self						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.image_processor_tester.prepare_image_processor_dict()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(a_    ,'image_mean'						)						)
						self.assertTrue(hasattr(a_    ,'image_std'						)						)
						self.assertTrue(hasattr(a_    ,'do_normalize'						)						)
						self.assertTrue(hasattr(a_    ,'do_resize'						)						)
						self.assertTrue(hasattr(a_    ,'do_rescale'						)						)
						self.assertTrue(hasattr(a_    ,'do_pad'						)						)
						self.assertTrue(hasattr(a_    ,'size'						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 18, 'longest_edge': 1333}						)
						self.assertEqual(image_processor.do_pad    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__		  =  image_processing(image_inputs[0]    ,return_tensors='pt'						).pixel_values

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_						)

						self.assertEqual(
						    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

						# Test batched
						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_    ,batched=a_						)

						lowerCAmelCase__		  =  image_processing(a_    ,return_tensors='pt'						).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_    ,numpify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__		  =  image_processing(image_inputs[0]    ,return_tensors='pt'						).pixel_values

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_						)

						self.assertEqual(
						    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

						# Test batched
						lowerCAmelCase__		  =  image_processing(a_    ,return_tensors='pt'						).pixel_values

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_    ,batched=a_						)

						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Initialize image_processing
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__		  =  prepare_image_inputs(self.image_processor_tester    ,equal_resolution=a_    ,torchify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__		  =  image_processing(image_inputs[0]    ,return_tensors='pt'						).pixel_values

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_						)

						self.assertEqual(
						    encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

						# Test batched
						lowerCAmelCase__		  =  image_processing(a_    ,return_tensors='pt'						).pixel_values

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.image_processor_tester.get_expected_values(a_    ,batched=a_						)

						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )    ,)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# prepare image and target
						lowerCAmelCase__		  =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,'r'						) as f:
										lowerCAmelCase__		  =  json.loads(f.read()						)

						lowerCAmelCase__		  =  {'image_id': 3_9769, 'annotations': target}

						# encode them
						lowerCAmelCase__		  =  DetaImageProcessor()
						lowerCAmelCase__		  =  image_processing(images=a_    ,annotations=a_    ,return_tensors='pt'						)

						# verify pixel values
						lowerCAmelCase__		  =  torch.Size([1, 3, 800, 1066]						)
						self.assertEqual(encoding['pixel_values'].shape    ,a_						)

						lowerCAmelCase__		  =  torch.tensor([0.2796, 0.3138, 0.3481]						)
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,a_    ,atol=1e-4						)						)

						# verify area
						lowerCAmelCase__		  =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,a_						)						)
						# verify boxes
						lowerCAmelCase__		  =  torch.Size([6, 4]						)
						self.assertEqual(encoding['labels'][0]['boxes'].shape    ,a_						)
						lowerCAmelCase__		  =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,a_    ,atol=1e-3						)						)
						# verify image_id
						lowerCAmelCase__		  =  torch.tensor([3_9769]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,a_						)						)
						# verify is_crowd
						lowerCAmelCase__		  =  torch.tensor([0, 0, 0, 0, 0, 0]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,a_						)						)
						# verify class_labels
						lowerCAmelCase__		  =  torch.tensor([75, 75, 63, 65, 17, 17]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,a_						)						)
						# verify orig_size
						lowerCAmelCase__		  =  torch.tensor([480, 640]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,a_						)						)
						# verify size
						lowerCAmelCase__		  =  torch.tensor([800, 1066]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,a_						)						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# prepare image, target and masks_path
						lowerCAmelCase__		  =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,'r'						) as f:
										lowerCAmelCase__		  =  json.loads(f.read()						)

						lowerCAmelCase__		  =  {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

						lowerCAmelCase__		  =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

						# encode them
						lowerCAmelCase__		  =  DetaImageProcessor(format='coco_panoptic'						)
						lowerCAmelCase__		  =  image_processing(images=a_    ,annotations=a_    ,masks_path=a_    ,return_tensors='pt'						)

						# verify pixel values
						lowerCAmelCase__		  =  torch.Size([1, 3, 800, 1066]						)
						self.assertEqual(encoding['pixel_values'].shape    ,a_						)

						lowerCAmelCase__		  =  torch.tensor([0.2796, 0.3138, 0.3481]						)
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,a_    ,atol=1e-4						)						)

						# verify area
						lowerCAmelCase__		  =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,a_						)						)
						# verify boxes
						lowerCAmelCase__		  =  torch.Size([6, 4]						)
						self.assertEqual(encoding['labels'][0]['boxes'].shape    ,a_						)
						lowerCAmelCase__		  =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,a_    ,atol=1e-3						)						)
						# verify image_id
						lowerCAmelCase__		  =  torch.tensor([3_9769]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,a_						)						)
						# verify is_crowd
						lowerCAmelCase__		  =  torch.tensor([0, 0, 0, 0, 0, 0]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,a_						)						)
						# verify class_labels
						lowerCAmelCase__		  =  torch.tensor([17, 17, 63, 75, 75, 93]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,a_						)						)
						# verify masks
						lowerCAmelCase__		  =  82_2873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,a_						)
						# verify orig_size
						lowerCAmelCase__		  =  torch.tensor([480, 640]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,a_						)						)
						# verify size
						lowerCAmelCase__		  =  torch.tensor([800, 1066]						)
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,a_						)						)





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
