





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  __A(						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"""

            _UpperCamelCase              =						size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
            _UpperCamelCase              =						parent
            _UpperCamelCase              =						batch_size
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						min_resolution
            _UpperCamelCase              =						max_resolution
            _UpperCamelCase              =						do_resize
            _UpperCamelCase              =						size
            _UpperCamelCase              =						do_normalize
            _UpperCamelCase              =						image_mean
            _UpperCamelCase              =						image_std
            _UpperCamelCase              =						do_rescale
            _UpperCamelCase              =						rescale_factor
            _UpperCamelCase              =						do_pad

     def 	_UpperCamelCase     (      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 	_UpperCamelCase     (      self,       A,       A=False      ):





            """simple docstring"""

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

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

            return expected_height, expected_width






@require_torch
@require_vision
class  __A(						__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =       DetaImageProcessor if is_vision_available() else None

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						DetaImageProcessingTester(self      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.image_processor_tester.prepare_image_processor_dict()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.image_processing_class(**self.image_processor_dict      )
            # create random PIL images
            _UpperCamelCase              =						prepare_image_inputs(self.image_processor_tester,       equal_resolution=A      )
            for image in image_inputs:
                   self.assertIsInstance(A,       Image.Image      )

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

            _UpperCamelCase       ,		_UpperCamelCase              =						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
            _UpperCamelCase       ,		_UpperCamelCase              =						self.image_processor_tester.get_expected_values(A,       batched=A      )

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.image_processing_class(**self.image_processor_dict      )
            # create random numpy tensors
            _UpperCamelCase              =						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
            _UpperCamelCase              =						image_processing(image_inputs[0],       return_tensors='''pt'''      ).pixel_values

            _UpperCamelCase       ,		_UpperCamelCase              =						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
            _UpperCamelCase              =						image_processing(A,       return_tensors='''pt'''      ).pixel_values

            _UpperCamelCase       ,		_UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.image_processing_class(**self.image_processor_dict      )
            # create random PyTorch tensors
            _UpperCamelCase              =						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
            _UpperCamelCase              =						image_processing(image_inputs[0],       return_tensors='''pt'''      ).pixel_values

            _UpperCamelCase       ,		_UpperCamelCase              =						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
            _UpperCamelCase              =						image_processing(A,       return_tensors='''pt'''      ).pixel_values

            _UpperCamelCase       ,		_UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
            with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',       '''r'''      ) as f:
                   _UpperCamelCase              =						json.loads(f.read()      )

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

            # encode them
            _UpperCamelCase              =						DetaImageProcessor()
            _UpperCamelCase              =						image_processing(images=A,       annotations=A,       return_tensors='''pt'''      )

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

            _UpperCamelCase              =						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],       A,       atol=1E-4      )      )

            # verify area
            _UpperCamelCase              =						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
            _UpperCamelCase              =						torch.Size([6, 4]      )
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,       A      )
            _UpperCamelCase              =						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],       A,       atol=1E-3      )      )
            # verify image_id
            _UpperCamelCase              =						torch.tensor([3_9769]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],       A      )      )
            # verify is_crowd
            _UpperCamelCase              =						torch.tensor([0, 0, 0, 0, 0, 0]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],       A      )      )
            # verify class_labels
            _UpperCamelCase              =						torch.tensor([75, 75, 63, 65, 17, 17]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],       A      )      )
            # verify orig_size
            _UpperCamelCase              =						torch.tensor([480, 640]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],       A      )      )
            # verify size
            _UpperCamelCase              =						torch.tensor([800, 1066]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],       A      )      )



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
            with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',       '''r'''      ) as f:
                   _UpperCamelCase              =						json.loads(f.read()      )

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

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

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

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

            _UpperCamelCase              =						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],       A,       atol=1E-4      )      )

            # verify area
            _UpperCamelCase              =						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
            _UpperCamelCase              =						torch.Size([6, 4]      )
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,       A      )
            _UpperCamelCase              =						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],       A,       atol=1E-3      )      )
            # verify image_id
            _UpperCamelCase              =						torch.tensor([3_9769]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],       A      )      )
            # verify is_crowd
            _UpperCamelCase              =						torch.tensor([0, 0, 0, 0, 0, 0]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],       A      )      )
            # verify class_labels
            _UpperCamelCase              =						torch.tensor([17, 17, 63, 75, 75, 93]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],       A      )      )
            # verify masks
            _UpperCamelCase              =						82_2873
            self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),       A      )
            # verify orig_size
            _UpperCamelCase              =						torch.tensor([480, 640]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],       A      )      )
            # verify size
            _UpperCamelCase              =						torch.tensor([800, 1066]      )
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],       A      )      )










import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

@is_pipeline_test
class  __A(						unittest.TestCase ):
     __A	  =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __A	  =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TextaTextGenerationPipeline(model=A,       tokenizer=A      )
            return generator, ["Something to write", "Something else"]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						generator('''Something there'''      )
            self.assertEqual(A,       [{'''generated_text''': ANY(A      )}]      )
            # These are encoder decoder, they don't just append to incoming string
            self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

            _UpperCamelCase              =						generator(['''This is great !''', '''Something else'''],       num_return_sequences=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            _UpperCamelCase              =						generator(
                ['''This is great !''', '''Something else'''],       num_return_sequences=2,       batch_size=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            with self.assertRaises(A      ):
                   generator(4      )

     @require_torch
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''pt'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )

            _UpperCamelCase              =						3
            _UpperCamelCase              =						generator(
                '''Something there''',       num_return_sequences=A,       num_beams=A,       )
            _UpperCamelCase              =						[
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': ''''''},
            ]
            self.assertEqual(A,       A      )

            _UpperCamelCase              =						generator('''This is a test''',       do_sample=A,       num_return_sequences=2,       return_tensors=A      )
            self.assertEqual(
                A,       [
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                ],       )
            _UpperCamelCase              =						generator.model.config.eos_token_id
            _UpperCamelCase              =						'''<pad>'''
            _UpperCamelCase              =						generator(
                ['''This is a test''', '''This is a second test'''],       do_sample=A,       num_return_sequences=2,       batch_size=2,       return_tensors=A,       )
            self.assertEqual(
                A,       [
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                ],       )



     @require_tf
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''tf'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )






