import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor


class 						lowercase    (     a     ):




							def 	__snake_case(						self   :					Dict      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.config_class(**self.inputs_dict      )
												self.parent.assertTrue(hasattr(_UpperCAmelCase	,							"tf_padding"      )      )
												self.parent.assertTrue(hasattr(_UpperCAmelCase	,							"depth_multiplier"      )      )



class 						lowercase    :




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					int=13	,							_UpperCamelCase   :					List[Any]=3	,							_UpperCamelCase   :					str=32	,							_UpperCamelCase   :					Any=0.2_5	,							_UpperCamelCase   :					Union[str, Any]=8	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Optional[int]=1_024	,							_UpperCamelCase   :					Optional[Any]=32	,							_UpperCamelCase   :					int="relu6"	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Union[str, Any]=0.0_2	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[Any]=True	,							_UpperCamelCase   :					Optional[Any]=10	,							_UpperCamelCase   :					Tuple=None	,							)					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       depth_multiplier
												SCREAMING_SNAKE_CASE    			=       min_depth
												SCREAMING_SNAKE_CASE    			=       tf_padding
												SCREAMING_SNAKE_CASE    			=       int(last_hidden_size * depth_multiplier      )
												SCREAMING_SNAKE_CASE    			=       output_stride
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       classifier_dropout_prob
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       num_labels
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       scope




							def 	__snake_case(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.num_labels      )
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,							self.num_labels      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

												return config, pixel_values, labels, pixel_labels




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												return MobileNetVaConfig(
												    num_channels=self.num_channels	,							image_size=self.image_size	,							depth_multiplier=self.depth_multiplier	,							min_depth=self.min_depth	,							tf_padding=self.tf_padding	,							hidden_act=self.hidden_act	,							classifier_dropout_prob=self.classifier_dropout_prob	,							initializer_range=self.initializer_range	,							)




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MobileNetVaModel(config=_UpperCAmelCase      )
												model.to(_UpperCAmelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCAmelCase      )
												self.parent.assertEqual(
												    result.last_hidden_state.shape	,							(
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )	,							)




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					str      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_labels
												SCREAMING_SNAKE_CASE    			=       MobileNetVaForImageClassification(_UpperCAmelCase      )
												model.to(_UpperCAmelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCAmelCase	,							labels=_UpperCAmelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )





							def 	__snake_case(						self   :					List[str]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Dict								=							(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							lowercase__						:       Union[str, Any]								=							(
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__						:       Any								=							False
							lowercase__						:       List[Any]								=							False
							lowercase__						:       Tuple								=							False
							lowercase__						:       str								=							False




							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MobileNetVaModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       MobileNetVaConfigTester(self	,							config_class=_UpperCAmelCase	,							has_text_modality=_UpperCAmelCase      )




							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"      )
							def 	__snake_case(						self   :					int      )					->				str:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"      )
							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[int]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileNetV1 does not output attentions"      )
							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCAmelCase      )




							def 	__snake_case(						self   :					Any      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCAmelCase      )




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					int      ):
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCAmelCase      )
																	model.to(_UpperCAmelCase      )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE    			=       model(**self._prepare_for_class(_UpperCAmelCase	,							_UpperCAmelCase      )      )

																	SCREAMING_SNAKE_CASE    			=       outputs.hidden_states

																	SCREAMING_SNAKE_CASE    			=       26
																	self.assertEqual(len(_UpperCAmelCase      )	,							_UpperCAmelCase      )

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       True
																	check_hidden_states_output(_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase      )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE    			=       True

																	check_hidden_states_output(_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase      )




							def 	__snake_case(						self   :					List[Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )





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



												'''simple docstring'''

												for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       MobileNetVaModel.from_pretrained(_UpperCAmelCase      )
																	self.assertIsNotNone(_UpperCAmelCase      )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


@require_torch
@require_vision
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					Tuple      )					->				Tuple:



												'''simple docstring'''

												return (
												    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"      ) if is_vision_available() else None
												)





							@slow
							def 	__snake_case(						self   :					Optional[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"      ).to(_UpperCAmelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCAmelCase	,							return_tensors="pt"      ).to(_UpperCAmelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCAmelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_001)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCAmelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]      ).to(_UpperCAmelCase      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,							_UpperCAmelCase	,							atol=1e-4      )      )

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					return [
					    int(1_0_0_0 * (box[0] / width)  ),
					    int(1_0_0_0 * (box[1] / height)  ),
					    int(1_0_0_0 * (box[2] / width)  ),
					    int(1_0_0_0 * (box[3] / height)  ),
					]





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							Optional[str]      , UpperCAmelCase__     :							Optional[str] = None  ):
					SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else ""

					# apply OCR
					SCREAMING_SNAKE_CASE    			=       to_pil_image(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       pil_image.size
					SCREAMING_SNAKE_CASE    			=       pytesseract.image_to_data(UpperCAmelCase__      , lang=UpperCAmelCase__      , output_type="dict"      , config=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       data["text"], data["left"], data["top"], data["width"], data["height"]

					# filter empty words and corresponding coordinates
					SCREAMING_SNAKE_CASE    			=       [idx for idx, word in enumerate(UpperCAmelCase__  ) if not word.strip()]
					SCREAMING_SNAKE_CASE    			=       [word for idx, word in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]

					# turn coordinates into (left, top, left+width, top+height) format
					SCREAMING_SNAKE_CASE    			=       []
					for x, y, w, h in zip(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       [x, y, x + w, y + h]
										actual_boxes.append(UpperCAmelCase__  )

					# finally, normalize the bounding boxes
					SCREAMING_SNAKE_CASE    			=       []
					for box in actual_boxes:
										normalized_boxes.append(normalize_box(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )  )

					assert len(UpperCAmelCase__  ) == len(UpperCAmelCase__  ), "Not as many words as there are bounding boxes"

					return words, normalized_boxes


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							["""pixel_values"""]




							def __init__(						self   :					int	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[str] = ""	,							**_UpperCamelCase   :					Optional[int]	,							)					->				None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Any	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"      )
												SCREAMING_SNAKE_CASE    			=       (size["height"], size["width"])
												return resize(_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )





							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					ChannelDimension = ChannelDimension.FIRST	,							**_UpperCamelCase   :					str	,							)					->				PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_SNAKE_CASE    			=       make_list_of_images(_UpperCamelCase      )

												if not valid_images(_UpperCamelCase      ):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray."      )

												if do_resize and size is None:
																	raise ValueError("Size must be specified if do_resize is True."      )

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(_UpperCamelCase      ) for image in images]

												if apply_ocr:
																	requires_backends(self	,							"pytesseract"      )
																	SCREAMING_SNAKE_CASE    			=       []
																	SCREAMING_SNAKE_CASE    			=       []
																	for image in images:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       apply_tesseract(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																						words_batch.append(_UpperCamelCase      )
																						boxes_batch.append(_UpperCamelCase      )

												if do_resize:
																	SCREAMING_SNAKE_CASE    			=       [self.resize(image=_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase      ) for image in images]

												# flip color channels from RGB to BGR (as Detectron2 requires this)
												SCREAMING_SNAKE_CASE    			=       [flip_channel_order(_UpperCamelCase      ) for image in images]
												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       BatchFeature(data={"pixel_values": images}	,							tensor_type=_UpperCamelCase      )

												if apply_ocr:
																	SCREAMING_SNAKE_CASE    			=       words_batch
																	SCREAMING_SNAKE_CASE    			=       boxes_batch
												return data




