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 ConditionalDetrImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	int     , a_     :	Tuple     , a_     :	Union[str, Any]=7     , a_     :	int=3     , a_     :	Optional[Any]=30     , a_     :	List[Any]=400     , a_     :	Tuple=True     , a_     :	Dict=None     , a_     :	Any=True     , a_     :	Union[str, Any]=[0.5, 0.5, 0.5]     , a_     :	Any=[0.5, 0.5, 0.5]     , a_     :	Dict=True     , a_     :	int=1 / 255     , a_     :	Tuple=True     , )->       Tuple:




       """simple docstring"""
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		parent
       SCREAMING_SNAKE_CASE__				:				List[str]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				int			=		do_resize
       SCREAMING_SNAKE_CASE__				:				List[str]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Tuple			=		image_mean
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_std
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		rescale_factor
       SCREAMING_SNAKE_CASE__				:				str			=		do_pad




  def 	__lowercase(      self     :	str				)->       Dict:




       """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 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	List[Any]=False				)->       List[Any]:




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

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

       return expected_height, expected_width




@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     ConditionalDetrImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	Dict				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		ConditionalDetrImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       List[str]:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	str				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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_     , 'size'				)				)




  def 	__lowercase(      self     :	int				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		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_				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.image_processing_class.from_dict(
           self.image_processor_dict     , size=42     , max_size=84     , pad_and_return_pixel_mask=a_				)
       self.assertEqual(image_processor.size     , {'shortest_edge': 42, 'longest_edge': 84}				)
       self.assertEqual(image_processor.do_pad     , a_				)




  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	List[Any]				)->       str:




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

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

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		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
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.image_processor_tester.get_expected_values(a_     , batched=a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		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 	__lowercase(      self     :	Tuple				)->       List[str]:




       """simple docstring"""
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_processing(image_inputs[0]     , return_tensors='pt'				).pixel_values

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		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
       SCREAMING_SNAKE_CASE__				:				Any			=		image_processing(a_     , return_tensors='pt'				).pixel_values

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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 	__lowercase(      self     :	List[Any]				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				int			=		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
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processing(image_inputs[0]     , return_tensors='pt'				).pixel_values

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		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
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processing(a_     , return_tensors='pt'				).pixel_values

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		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 	__lowercase(      self     :	List[str]				)->       Tuple:




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

       SCREAMING_SNAKE_CASE__				:				str			=		{'image_id': 3_9769, 'annotations': target}

       # encode them
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processing(images=a_     , annotations=a_     , return_tensors='pt'				)

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		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
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']     , a_				)				)
       # verify boxes
       SCREAMING_SNAKE_CASE__				:				str			=		torch.Size([6, 4]				)
       self.assertEqual(encoding['labels'][0]['boxes'].shape     , a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		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
       SCREAMING_SNAKE_CASE__				:				str			=		torch.tensor([3_9769]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     , a_				)				)
       # verify is_crowd
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     , a_				)				)
       # verify class_labels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([75, 75, 63, 65, 17, 17]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     , a_				)				)
       # verify orig_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     , a_				)				)
       # verify size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']     , a_				)				)




  @slow
  def 	__lowercase(      self     :	str				)->       Tuple:




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

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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

       # encode them
       SCREAMING_SNAKE_CASE__				:				List[str]			=		ConditionalDetrImageProcessor(format='coco_panoptic'				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_processing(images=a_     , annotations=a_     , masks_path=a_     , return_tensors='pt'				)

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		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
       SCREAMING_SNAKE_CASE__				:				int			=		torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']     , a_				)				)
       # verify boxes
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.Size([6, 4]				)
       self.assertEqual(encoding['labels'][0]['boxes'].shape     , a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([3_9769]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     , a_				)				)
       # verify is_crowd
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     , a_				)				)
       # verify class_labels
       SCREAMING_SNAKE_CASE__				:				str			=		torch.tensor([17, 17, 63, 75, 75, 93]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     , a_				)				)
       # verify masks
       SCREAMING_SNAKE_CASE__				:				Tuple			=		82_2873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()     , a_				)
       # verify orig_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     , a_				)				)
       # verify size
       SCREAMING_SNAKE_CASE__				:				str			=		torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']     , a_				)				)




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if is_torch_version('<'     ,  '2.0.0' ) or not hasattr(lowercase__     ,  '_dynamo' ):
          return False
     return isinstance(lowercase__     ,  torch._dynamo.eval_frame.OptimizedModule )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

     if not keep_fpaa_wrapper:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  'forward' )
          SCREAMING_SNAKE_CASE__				:				str			=		model.__dict__.pop('_original_forward'     ,  lowercase__ )
          if original_forward is not None:
               while hasattr(lowercase__     ,  '__wrapped__' ):
                    SCREAMING_SNAKE_CASE__				:				Dict			=		forward.__wrapped__
                    if forward == original_forward:
                         break
               SCREAMING_SNAKE_CASE__				:				Dict			=		forward
          if getattr(lowercase__     ,  '_converted_to_transformer_engine'     ,  lowercase__ ):
               convert_model(lowercase__     ,  to_transformer_engine=lowercase__ )

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase__     ,  lowercase__ )
     elif PartialState().local_process_index == 0:
          torch.save(lowercase__     ,  lowercase__ )







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



