

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
			import jax

			from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			Tuple   ,   a				:			int   ,   a				:			Optional[int]=13   ,   a				:			Dict=30   ,   a				:			Optional[int]=2   ,   a				:			Dict=3   ,   a				:			str=True   ,   a				:			Tuple=True   ,   a				:			int=32   ,   a				:			List[Any]=5   ,   a				:			Tuple=4   ,   a				:			Tuple=37   ,   a				:			Optional[Any]="gelu"   ,   a				:			int=0.1   ,   a				:			Optional[int]=0.1   ,   a				:			Optional[int]=10   ,   a				:			List[str]=0.02   ,   )				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							parent
				SCREAMING_SNAKE_CASE :   Tuple					  =							batch_size
				SCREAMING_SNAKE_CASE :   int					  =							image_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							patch_size
				SCREAMING_SNAKE_CASE :   Dict					  =							num_channels
				SCREAMING_SNAKE_CASE :   str					  =							is_training
				SCREAMING_SNAKE_CASE :   List[str]					  =							use_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Any					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Any					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   str					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							initializer_range

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE :   List[str]					  =							(image_size // patch_size) ** 2
				SCREAMING_SNAKE_CASE :   int					  =							num_patches + 1
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							ViTConfig(
				    image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   is_decoder=a   ,   initializer_range=self.initializer_range   ,   )

				return config, pixel_values
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							FlaxViTModel(config=a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(a						)
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE :   Dict					  =							(self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE :   int					  =							(self.patch_size, self.patch_size)
				SCREAMING_SNAKE_CASE :   int					  =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, num_patches + 1, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			Union[str, Any]   ,   a				:			Any						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							FlaxViTForImageClassification(config=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							model(a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)						)

				# test greyscale images
				SCREAMING_SNAKE_CASE :   str					  =							1
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FlaxViTForImageClassification(a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				SCREAMING_SNAKE_CASE :   Dict					  =							model(a						)






			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.prepare_config_and_inputs()
				(
				    (
				    SCREAMING_SNAKE_CASE
				)  ,(
				    SCREAMING_SNAKE_CASE
				)  ,
				) :   Any					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_flax
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FlaxViTModelTester(self						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							ConfigTester(self   ,   config_class=a   ,   has_text_modality=a   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			str						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							model_class(a						)
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							inspect.signature(model.__call__						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE :   Tuple					  =							[*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE :   List[Any]					  =							["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,   a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
						SCREAMING_SNAKE_CASE :   List[Any]					  =							self._prepare_for_class(a   ,   a						)
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model_class(a						)

						@jax.jit
						def model_jitted(a				:			Any   ,   **a				:			Any						):
							return model(pixel_values=a   ,   **a						)

						with self.subTest("JIT Enabled"						):
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model_jitted(**a						).to_tuple()

						with self.subTest("JIT Disabled"						):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE :   Any					  =							model_jitted(**a						).to_tuple()

						self.assertEqual(len(a						)   ,   len(a						)						)
						for jitted_output, output in zip(a   ,   a						):
							self.assertEqual(jitted_output.shape   ,   output.shape						)






			@slow
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							model_class_name.from_pretrained("google/vit-base-patch16-224"						)
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(np.ones((1, 3, 224, 224)						)						)
					self.assertIsNotNone(a						)


import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:






			class _UpperCamelCase				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			str   ,   **a				:			int						)				->							str:

							"""simple docstring"""

							pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_OBJECT_DETECTION_MAPPING
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ObjectDetectionPipeline(model=a   ,   image_processor=a						)
				return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"   ,   threshold=0.0						)

				self.assertGreater(len(a						)   ,   0						)
				for detected_object in outputs:
					self.assertEqual(
					    a   ,   {
					        "score": ANY(a						),
					        "label": ANY(a						),
					        "box": {"xmin": ANY(a						), "ymin": ANY(a						), "xmax": ANY(a						), "ymax": ANY(a						)},
					    }   ,   )

				import datasets

				SCREAMING_SNAKE_CASE :   Any					  =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,   "image"   ,   split="test"						)

				SCREAMING_SNAKE_CASE :   Dict					  =							[
				    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
				    "http://images.cocodataset.org/val2017/000000039769.jpg",
				    # RGBA
				    dataset[0]["file"],
				    # LA
				    dataset[1]["file"],
				    # L
				    dataset[2]["file"],
				]
				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector(a   ,   threshold=0.0						)

				self.assertEqual(len(a						)   ,   len(a						)						)
				for outputs in batch_outputs:
					self.assertGreater(len(a						)   ,   0						)
					for detected_object in outputs:
						self.assertEqual(
						    a   ,   {
						        "score": ANY(a						),
						        "label": ANY(a						),
						        "box": {"xmin": ANY(a						), "ymin": ANY(a						), "xmax": ANY(a						), "ymax": ANY(a						)},
						    }   ,   )
			@require_tf
			@unittest.skip("Object detection not implemented in TF"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				pass
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"hf-internal-testing/tiny-detr-mobilenetsv3"

				SCREAMING_SNAKE_CASE :   Dict					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   int					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=0.0						)

				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]   ,   threshold=0.0   ,   )

				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        ],
				        [
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   int					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0.9985
				SCREAMING_SNAKE_CASE :   int					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   List[str]					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   str					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=a						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )






			@require_torch
			@require_pytesseract
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							"Narsil/layoutlmv3-finetuned-funsd"
				SCREAMING_SNAKE_CASE :   Dict					  =							0.9993

				SCREAMING_SNAKE_CASE :   str					  =							pipeline("object-detection"   ,   model=a   ,   threshold=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							object_detector(
				    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
				        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
				    ]   ,   )