





'''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 YolosImageProcessor


class   _lowercase						(							unittest.TestCase				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=7    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=30    ,   _UpperCAmelCase=400    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=[0.5, 0.5, 0.5]    ,   _UpperCAmelCase=[0.5, 0.5, 0.5]    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=1 / 255    ,   _UpperCAmelCase=True    ,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       A       :       Optional[Any]							=						size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
       A       :       Any							=						parent
       A       :       List[Any]							=						batch_size
       A       :       int							=						num_channels
       A       :       Optional[int]							=						min_resolution
       A       :       Any							=						max_resolution
       A       :       Optional[Any]							=						do_resize
       A       :       Tuple							=						size
       A       :       int							=						do_normalize
       A       :       Optional[int]							=						image_mean
       A       :       Optional[int]							=						image_std
       A       :       List[str]							=						do_rescale
       A       :       Dict							=						rescale_factor
       A       :       List[Any]							=						do_pad


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

       else:
           A       :       List[Any]							=						[]
           for image in image_inputs:
               A,	A       :       List[Any]							=						self.get_expected_values([image]					)
               expected_values.append((expected_height, expected_width)					)
           A       :       List[Any]							=						max(_UpperCAmelCase    ,   key=lambda _UpperCAmelCase					: item[0]					)[0]
           A       :       List[str]							=						max(_UpperCAmelCase    ,   key=lambda _UpperCAmelCase					: item[1]					)[1]

       return expected_height, expected_width







@require_torch
@require_vision
class   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   YolosImageProcessor if is_vision_available() else None


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						YolosImageProcessingTester(self					)


   @property
   def 					snake_case				( self					):
       return self.image_processor_tester.prepare_image_processor_dict()


   def 					snake_case				( self					):
       A       :       List[str]							=						self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(_UpperCAmelCase    ,   '''image_mean'''					)					)
       self.assertTrue(hasattr(_UpperCAmelCase    ,   '''image_std'''					)					)
       self.assertTrue(hasattr(_UpperCAmelCase    ,   '''do_normalize'''					)					)
       self.assertTrue(hasattr(_UpperCAmelCase    ,   '''do_resize'''					)					)
       self.assertTrue(hasattr(_UpperCAmelCase    ,   '''size'''					)					)


   def 					snake_case				( self					):
       A       :       int							=						self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 18, '''longest_edge''': 1_333}					)
       self.assertEqual(image_processor.do_pad    ,   _UpperCAmelCase					)

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


   def 					snake_case				( self					):
       pass


   def 					snake_case				( self					):
       # Initialize image_processing
       A       :       List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       A       :       Optional[Any]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=_UpperCAmelCase					)
       for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase    ,   Image.Image					)

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

       A,	A       :       str							=						self.image_processor_tester.get_expected_values(_UpperCAmelCase					)

       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(_UpperCAmelCase    ,   batched=_UpperCAmelCase					)

       A       :       Optional[Any]							=						image_processing(_UpperCAmelCase    ,   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 					snake_case				( self					):
       # Initialize image_processing
       A       :       Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       A       :       Dict							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=_UpperCAmelCase    ,   numpify=_UpperCAmelCase					)
       for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase    ,   np.ndarray					)

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

       A,	A       :       Union[str, Any]							=						self.image_processor_tester.get_expected_values(_UpperCAmelCase					)

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

       # Test batched
       A       :       Union[str, Any]							=						image_processing(_UpperCAmelCase    ,   return_tensors='''pt'''					).pixel_values

       A,	A       :       int							=						self.image_processor_tester.get_expected_values(_UpperCAmelCase    ,   batched=_UpperCAmelCase					)

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


   def 					snake_case				( self					):
       # Initialize image_processing
       A       :       Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       A       :       List[Any]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=_UpperCAmelCase    ,   torchify=_UpperCAmelCase					)
       for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase    ,   torch.Tensor					)

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

       A,	A       :       Union[str, Any]							=						self.image_processor_tester.get_expected_values(_UpperCAmelCase					)

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

       # Test batched
       A       :       List[str]							=						image_processing(_UpperCAmelCase    ,   return_tensors='''pt'''					).pixel_values

       A,	A       :       List[str]							=						self.image_processor_tester.get_expected_values(_UpperCAmelCase    ,   batched=_UpperCAmelCase					)

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


   def 					snake_case				( self					):
       # Initialize image_processings
       A       :       str							=						self.image_processing_class(**self.image_processor_dict					)
       A       :       int							=						self.image_processing_class(do_resize=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   do_rescale=_UpperCAmelCase					)
       # create random PyTorch tensors
       A       :       Any							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=_UpperCAmelCase    ,   torchify=_UpperCAmelCase					)
       for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase    ,   torch.Tensor					)

       # Test whether the method "pad" and calling the image processor return the same tensors
       A       :       Optional[int]							=						image_processing_a.pad(_UpperCAmelCase    ,   return_tensors='''pt'''					)
       A       :       str							=						image_processing_a(_UpperCAmelCase    ,   return_tensors='''pt'''					)

       self.assertTrue(
           torch.allclose(encoded_images_with_method['''pixel_values''']    ,   encoded_images['''pixel_values''']    ,   atol=1E-4					)					)


   @slow
   def 					snake_case				( self					):
       # prepare image and target
       A       :       Union[str, Any]							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,   '''r'''					) as f:
           A       :       Union[str, Any]							=						json.loads(f.read()					)

       A       :       Any							=						{'''image_id''': 39_769, '''annotations''': target}

       # encode them
       A       :       Optional[int]							=						YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''					)
       A       :       Optional[int]							=						image_processing(images=_UpperCAmelCase    ,   annotations=_UpperCAmelCase    ,   return_tensors='''pt'''					)

       # verify pixel values
       A       :       Optional[int]							=						torch.Size([1, 3, 800, 1_066]					)
       self.assertEqual(encoding['''pixel_values'''].shape    ,   _UpperCAmelCase					)

       A       :       Any							=						torch.tensor([0.2796, 0.3138, 0.3481]					)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)

       # verify area
       A       :       Tuple							=						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   _UpperCAmelCase					)					)
       # verify boxes
       A       :       List[Any]							=						torch.Size([6, 4]					)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   _UpperCAmelCase					)
       A       :       List[Any]							=						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   _UpperCAmelCase    ,   atol=1E-3					)					)
       # verify image_id
       A       :       str							=						torch.tensor([39_769]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   _UpperCAmelCase					)					)
       # verify is_crowd
       A       :       Optional[int]							=						torch.tensor([0, 0, 0, 0, 0, 0]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   _UpperCAmelCase					)					)
       # verify class_labels
       A       :       List[Any]							=						torch.tensor([75, 75, 63, 65, 17, 17]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   _UpperCAmelCase					)					)
       # verify orig_size
       A       :       Optional[int]							=						torch.tensor([480, 640]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   _UpperCAmelCase					)					)
       # verify size
       A       :       Dict							=						torch.tensor([800, 1_066]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   _UpperCAmelCase					)					)


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

       A       :       Dict							=						{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

       A       :       List[Any]							=						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

       # encode them
       A       :       int							=						YolosImageProcessor(format='''coco_panoptic'''					)
       A       :       List[str]							=						image_processing(images=_UpperCAmelCase    ,   annotations=_UpperCAmelCase    ,   masks_path=_UpperCAmelCase    ,   return_tensors='''pt'''					)

       # verify pixel values
       A       :       Tuple							=						torch.Size([1, 3, 800, 1_066]					)
       self.assertEqual(encoding['''pixel_values'''].shape    ,   _UpperCAmelCase					)

       A       :       Dict							=						torch.tensor([0.2796, 0.3138, 0.3481]					)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   _UpperCAmelCase    ,   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''']    ,   _UpperCAmelCase					)					)
       # verify boxes
       A       :       Dict							=						torch.Size([6, 4]					)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   _UpperCAmelCase					)
       A       :       Tuple							=						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   _UpperCAmelCase    ,   atol=1E-3					)					)
       # verify image_id
       A       :       List[Any]							=						torch.tensor([39_769]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   _UpperCAmelCase					)					)
       # verify is_crowd
       A       :       List[Any]							=						torch.tensor([0, 0, 0, 0, 0, 0]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   _UpperCAmelCase					)					)
       # verify class_labels
       A       :       Dict							=						torch.tensor([17, 17, 63, 75, 75, 93]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   _UpperCAmelCase					)					)
       # verify masks
       A       :       str							=						822_873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   _UpperCAmelCase					)
       # verify orig_size
       A       :       List[Any]							=						torch.tensor([480, 640]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   _UpperCAmelCase					)					)
       # verify size
       A       :       Dict							=						torch.tensor([800, 1_066]					)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   _UpperCAmelCase					)					)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
