
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
	from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image







class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=13		,    snake_case=64		,    snake_case=2		,    snake_case=3		,    snake_case=True		,    snake_case=True		,    snake_case=32		,    snake_case=5		,    snake_case=4		,    snake_case=37		,    snake_case="gelu"		,    snake_case=0.1		,    snake_case=0.1		,    snake_case=10		,    snake_case=0.02		,    snake_case=[1, 16, 4, 4]		,    snake_case=None		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    patch_size
		lowercase       =    num_channels
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    hidden_size
		lowercase       =    num_hidden_layers
		lowercase       =    num_attention_heads
		lowercase       =    intermediate_size
		lowercase       =    hidden_act
		lowercase       =    hidden_dropout_prob
		lowercase       =    attention_probs_dropout_prob
		lowercase       =    type_sequence_label_size
		lowercase       =    initializer_range
		lowercase       =    scope
		lowercase       =    backbone_featmap_shape

		# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
		# the number of patches is based on the feature map of the backbone, which by default uses an output stride
		# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
		lowercase       =    (self.image_size // 32) ** 2
		lowercase       =    num_patches + 1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		lowercase       =    None
		if self.use_labels:
			lowercase       =    ids_tensor([self.batch_size]		,    self.type_sequence_label_size       )

		lowercase       =    self.get_config()

		return config, pixel_values, labels





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    {
		    'global_padding': 'same',
		    'layer_type': 'bottleneck',
		    'depths': [3, 4, 9],
		    'out_features': ['stage1', 'stage2', 'stage3'],
		    'embedding_dynamic_padding': True,
		    'hidden_sizes': [4, 8, 16, 32],
		    'num_groups': 2,
		}

		return ViTHybridConfig(
		    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=snake_case		,    initializer_range=self.initializer_range		,    backbone_featmap_shape=self.backbone_featmap_shape		,    backbone_config=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    ViTHybridModel(config=snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case       )
		self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    self.type_sequence_label_size
		lowercase       =    ViTHybridForImageClassification(snake_case       )
		model.to(snake_case       )
		model.eval()
		lowercase       =    model(snake_case		,    labels=snake_case       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.prepare_config_and_inputs()
		lowercase  ,					lowercase  ,					lowercase       =    config_and_inputs
		lowercase       =    {'pixel_values': pixel_values}
		return config, inputs_dict







@require_torch
class        A_   (			__lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Union[str, Any]							 =       (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
	_UpperCamelCase     :     str							 =       (
	    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
	    if is_torch_available()
	    else {}
	)
	_UpperCamelCase     :     Dict							 =       False
	_UpperCamelCase     :     Optional[Any]							 =       False
	_UpperCamelCase     :     Union[str, Any]							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ViTHybridModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=snake_case		,    has_text_modality=snake_case		,    hidden_size=37       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.config_tester.run_common_tests()





	@unittest.skip(reason='ViT does not use inputs_embeds'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase       =    model_class(snake_case       )
			self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)       )
			lowercase       =    model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(snake_case		,    nn.Linear       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase       =    ['pixel_values']
			self.assertListEqual(arg_names[:1]		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		lowercase       =    _config_zero_init(snake_case       )
		for model_class in self.all_model_classes:
			lowercase       =    model_class(config=snake_case       )
			# Skip the check for the backbone
			for name, module in model.named_modules():
				if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
					lowercase       =    [F'''{name}.{key}''' for key in module.state_dict().keys()]
					break

			for name, param in model.named_parameters():
				if param.requires_grad:
					if name in backbone_params:
						continue
					self.assertIn(
					    ((param.data.mean() * 1E9).round() / 1E9).item()		,    [0.0, 1.0]		,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''		,    )





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase       =    ViTHybridModel.from_pretrained(snake_case       )
			self.assertIsNotNone(snake_case       )






def 							UpperCAmelCase_		(							):
	lowercase       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







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




	'''simple docstring'''


	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return (
		    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
		    if is_vision_available()
		    else None
		)





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
		    snake_case       )

		lowercase       =    self.default_image_processor
		lowercase       =    prepare_img()
		lowercase       =    image_processor(images=snake_case		,    return_tensors='pt'       ).to(snake_case       )

		# forward pass
		with torch.no_grad():
			lowercase       =    model(**snake_case       )

		# verify the logits
		lowercase       =    torch.Size((1, 1000)       )
		self.assertEqual(outputs.logits.shape		,    snake_case       )

		lowercase       =    torch.tensor([-1.9_090, -0.4_993, -0.2_389]       ).to(snake_case       )

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





	@slow
	@require_accelerate
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'       )
		lowercase       =    ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'		,    device_map='auto'       )

		lowercase       =    prepare_img()

		lowercase       =    image_processor(images=snake_case		,    return_tensors='pt'       )
		lowercase       =    model(**snake_case       )
		lowercase       =    outputs.logits
		# model predicts one of the 1000 ImageNet classes
		lowercase       =    logits.argmax(-1       ).item()

		self.assertTrue(model.config.idalabel[predicted_class_idx]		,    'tabby, tabby cat'       )



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	return [
	    int(1000 * (box[0] / width)							),
	    int(1000 * (box[1] / height)							),
	    int(1000 * (box[2] / width)							),
	    int(1000 * (box[3] / height)							),
	]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = ""		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_rescale
		lowercase       =    rescale_value
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		lowercase       =    image_std if image_std is not None else IMAGENET_STANDARD_STD
		lowercase       =    apply_ocr
		lowercase       =    ocr_lang
		lowercase       =    tesseract_config





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		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()}'''       )
		lowercase       =    (size['height'], size['width'])
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(snake_case       ):
			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.'       )

		if do_rescale and rescale_factor is None:
			raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'       )

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

		# Tesseract OCR to get words + normalized bounding boxes
		if apply_ocr:
			requires_backends(self		,    'pytesseract'       )
			lowercase       =    []
			lowercase       =    []
			for image in images:
				lowercase  ,					lowercase       =    apply_tesseract(snake_case		,    snake_case		,    snake_case       )
				words_batch.append(snake_case       )
				boxes_batch.append(snake_case       )

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

		if do_rescale:
			lowercase       =    [self.rescale(image=snake_case		,    scale=snake_case       ) for image in images]

		if do_normalize:
			lowercase       =    [self.normalize(image=snake_case		,    mean=snake_case		,    std=snake_case       ) for image in images]

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

