



'''simple docstring'''






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 							a__(      unittest.TestCase			):







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						Dict		,    __snake_case				:						Union[str, Any]=7		,    __snake_case				:						str=3		,    __snake_case				:						Optional[int]=30		,    __snake_case				:						Dict=4_00		,    __snake_case				:						List[Any]=True		,    __snake_case				:						Any=None		,    __snake_case				:						Dict=True		,    __snake_case				:						Optional[Any]=[0.5, 0.5, 0.5]		,    __snake_case				:						Union[str, Any]=[0.5, 0.5, 0.5]		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						List[str]=1 / 2_55		,    __snake_case				:						Optional[int]=True		,    ):
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      a		:      Union[str, Any]       =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
      a		:      List[str]       =  parent
      a		:      List[Any]       =  batch_size
      a		:      Union[str, Any]       =  num_channels
      a		:      int       =  min_resolution
      a		:      Dict       =  max_resolution
      a		:      Union[str, Any]       =  do_resize
      a		:      Optional[int]       =  size
      a		:      int       =  do_normalize
      a		:      Optional[int]       =  image_mean
      a		:      str       =  image_std
      a		:      Optional[int]       =  do_rescale
      a		:      Any       =  rescale_factor
      a		:      Optional[int]       =  do_pad







    def     lowercase_ (   self				:						Union[str, Any]			):
      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				:						Optional[Any]		,    __snake_case				:						Tuple		,    __snake_case				:						int=False			):
      if not batched:
        a		:      Optional[int]       =  image_inputs[0]
        if isinstance(__snake_case		,    Image.Image			):
          a     ,			a		:      Optional[int]       =  image.size
        else:
          a     ,			a		:      Any       =  image.shape[1], image.shape[2]
        if w < h:
          a		:      Tuple       =  int(self.size['shortest_edge'] * h / w			)
          a		:      Union[str, Any]       =  self.size['shortest_edge']
        elif w > h:
          a		:      List[str]       =  self.size['shortest_edge']
          a		:      List[str]       =  int(self.size['shortest_edge'] * w / h			)
        else:
          a		:      int       =  self.size['shortest_edge']
          a		:      str       =  self.size['shortest_edge']

      else:
        a		:      int       =  []
        for image in image_inputs:
          a     ,			a		:      Optional[Any]       =  self.get_expected_values([image]			)
          expected_values.append((expected_height, expected_width)			)
        a		:      Optional[int]       =  max(__snake_case		,    key=lambda __snake_case			: item[0]			)[0]
        a		:      Tuple       =  max(__snake_case		,    key=lambda __snake_case			: item[1]			)[1]

      return expected_height, expected_width

@require_torch
@require_vision
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					ConditionalDetrImageProcessor if is_vision_available() else None







    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ConditionalDetrImageProcessingTester(self			)







    @property
    def     lowercase_ (   self				:						Tuple			):
      return self.image_processor_tester.prepare_image_processor_dict()







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      int       =  self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(__snake_case		,    'image_mean'			)			)
      self.assertTrue(hasattr(__snake_case		,    'image_std'			)			)
      self.assertTrue(hasattr(__snake_case		,    'do_normalize'			)			)
      self.assertTrue(hasattr(__snake_case		,    'do_resize'			)			)
      self.assertTrue(hasattr(__snake_case		,    'size'			)			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      List[str]       =  self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size		,    {'shortest_edge': 18, 'longest_edge': 13_33}			)
      self.assertEqual(image_processor.do_pad		,    __snake_case			)

      a		:      Union[str, Any]       =  self.image_processing_class.from_dict(
          self.image_processor_dict		,    size=42		,    max_size=84		,    pad_and_return_pixel_mask=__snake_case			)
      self.assertEqual(image_processor.size		,    {'shortest_edge': 42, 'longest_edge': 84}			)
      self.assertEqual(image_processor.do_pad		,    __snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      pass







    def     lowercase_ (   self				:						Optional[int]			):
      # Initialize image_processing
      a		:      Union[str, Any]       =  self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      a		:      Dict       =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__snake_case			)
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,    Image.Image			)

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

      a     ,			a		:      Optional[int]       =  self.image_processor_tester.get_expected_values(__snake_case			)

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

      # Test batched
      a     ,			a		:      int       =  self.image_processor_tester.get_expected_values(__snake_case		,    batched=__snake_case			)

      a		:      Optional[int]       =  image_processing(__snake_case		,    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				:						Any			):
      # Initialize image_processing
      a		:      List[str]       =  self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      a		:      List[Any]       =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__snake_case		,    numpify=__snake_case			)
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,    np.ndarray			)

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

      a     ,			a		:      str       =  self.image_processor_tester.get_expected_values(__snake_case			)

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

      # Test batched
      a		:      Optional[int]       =  image_processing(__snake_case		,    return_tensors='pt'			).pixel_values

      a     ,			a		:      Optional[int]       =  self.image_processor_tester.get_expected_values(__snake_case		,    batched=__snake_case			)

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







    def     lowercase_ (   self				:						int			):
      # Initialize image_processing
      a		:      Dict       =  self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      a		:      int       =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__snake_case		,    torchify=__snake_case			)
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,    torch.Tensor			)

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

      a     ,			a		:      Optional[Any]       =  self.image_processor_tester.get_expected_values(__snake_case			)

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

      # Test batched
      a		:      Tuple       =  image_processing(__snake_case		,    return_tensors='pt'			).pixel_values

      a     ,			a		:      Union[str, Any]       =  self.image_processor_tester.get_expected_values(__snake_case		,    batched=__snake_case			)

      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				:						str			):
      # prepare image and target
      a		:      str       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,    'r'			) as f:
        a		:      List[Any]       =  json.loads(f.read()			)

      a		:      List[str]       =  {'image_id': 3_97_69, 'annotations': target}

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

      # verify pixel values
      a		:      Union[str, Any]       =  torch.Size([1, 3, 8_00, 10_66]			)
      self.assertEqual(encoding['pixel_values'].shape		,    __snake_case			)

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

      # verify area
      a		:      int       =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,    __snake_case			)			)
      # verify boxes
      a		:      str       =  torch.Size([6, 4]			)
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,    __snake_case			)
      a		:      Any       =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,    __snake_case		,    atol=1e-3			)			)
      # verify image_id
      a		:      Union[str, Any]       =  torch.tensor([3_97_69]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,    __snake_case			)			)
      # verify is_crowd
      a		:      Union[str, Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,    __snake_case			)			)
      # verify class_labels
      a		:      List[str]       =  torch.tensor([75, 75, 63, 65, 17, 17]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,    __snake_case			)			)
      # verify orig_size
      a		:      Union[str, Any]       =  torch.tensor([4_80, 6_40]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,    __snake_case			)			)
      # verify size
      a		:      List[str]       =  torch.tensor([8_00, 10_66]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,    __snake_case			)			)







    @slow
    def     lowercase_ (   self				:						int			):
      # prepare image, target and masks_path
      a		:      Any       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,    'r'			) as f:
        a		:      Optional[int]       =  json.loads(f.read()			)

      a		:      int       =  {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

      a		:      Dict       =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

      # encode them
      a		:      Optional[Any]       =  ConditionalDetrImageProcessor(format='coco_panoptic'			)
      a		:      Optional[int]       =  image_processing(images=__snake_case		,    annotations=__snake_case		,    masks_path=__snake_case		,    return_tensors='pt'			)

      # verify pixel values
      a		:      Dict       =  torch.Size([1, 3, 8_00, 10_66]			)
      self.assertEqual(encoding['pixel_values'].shape		,    __snake_case			)

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

      # verify area
      a		:      int       =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,    __snake_case			)			)
      # verify boxes
      a		:      List[Any]       =  torch.Size([6, 4]			)
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,    __snake_case			)
      a		:      List[str]       =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,    __snake_case		,    atol=1e-3			)			)
      # verify image_id
      a		:      List[Any]       =  torch.tensor([3_97_69]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,    __snake_case			)			)
      # verify is_crowd
      a		:      List[Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,    __snake_case			)			)
      # verify class_labels
      a		:      Tuple       =  torch.tensor([17, 17, 63, 75, 75, 93]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,    __snake_case			)			)
      # verify masks
      a		:      List[Any]       =  82_28_73
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,    __snake_case			)
      # verify orig_size
      a		:      List[Any]       =  torch.tensor([4_80, 6_40]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,    __snake_case			)			)
      # verify size
      a		:      Tuple       =  torch.tensor([8_00, 10_66]			)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,    __snake_case			)			)




'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)

lowerCAmelCase:      List[str]		 =		{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					"""imagegpt"""
    lowercase__				=					["""past_key_values"""]
    lowercase__				=					{
        """hidden_size""": """n_embd""",
        """max_position_embeddings""": """n_positions""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(   self				:						List[str]		,    __snake_case				:						int=5_12 + 1		,    __snake_case				:						Optional[Any]=32 * 32		,    __snake_case				:						Optional[Any]=5_12		,    __snake_case				:						List[str]=24		,    __snake_case				:						Tuple=8		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						Dict="quick_gelu"		,    __snake_case				:						Dict=0.1		,    __snake_case				:						List[str]=0.1		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						Tuple=1e-5		,    __snake_case				:						Union[str, Any]=0.02		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						Dict=True		,    __snake_case				:						str=False		,    __snake_case				:						Any=False		,    __snake_case				:						List[Any]=False		,    **__snake_case				:						List[str]		,    ):
      a		:      Optional[Any]       =  vocab_size
      a		:      Union[str, Any]       =  n_positions
      a		:      int       =  n_embd
      a		:      int       =  n_layer
      a		:      str       =  n_head
      a		:      int       =  n_inner
      a		:      int       =  activation_function
      a		:      Any       =  resid_pdrop
      a		:      Tuple       =  embd_pdrop
      a		:      List[Any]       =  attn_pdrop
      a		:      Tuple       =  layer_norm_epsilon
      a		:      Any       =  initializer_range
      a		:      str       =  scale_attn_weights
      a		:      List[Any]       =  use_cache
      a		:      Any       =  scale_attn_by_inverse_layer_idx
      a		:      Optional[Any]       =  reorder_and_upcast_attn
      a		:      str       =  tie_word_embeddings

      super().__init__(tie_word_embeddings=__snake_case		,    **__snake_case			)

class 							a__(      lowerCamelCase__			):







    @property
    def     lowercase_ (   self				:						Union[str, Any]			):
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
          ]			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						"FeatureExtractionMixin"		,    __snake_case				:						int = 1		,    __snake_case				:						int = -1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional["TensorType"] = None		,    __snake_case				:						int = 3		,    __snake_case				:						int = 32		,    __snake_case				:						int = 32		,    ):
      a		:      Dict       =  self._generate_dummy_images(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)
      a		:      Union[str, Any]       =  dict(preprocessor(images=__snake_case		,    return_tensors=__snake_case			)			)

      return inputs