'''simple docstring'''


import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 torch import nn

       from transformers import RegNetForImageClassification, RegNetModel
       from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor





class       _a    :


  """simple docstring"""

  def __init__(		self      :       Tuple   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       List[Any]=3   ,				lowercase_      :       Any=32   ,				lowercase_      :       Union[str, Any]=3   ,				lowercase_      :       Optional[int]=10   ,				lowercase_      :       List[Any]=[10, 20, 30, 40]   ,				lowercase_      :       Optional[int]=[1, 1, 2, 1]   ,				lowercase_      :       Any=True   ,				lowercase_      :       Union[str, Any]=True   ,				lowercase_      :       List[str]="relu"   ,				lowercase_      :       Union[str, Any]=3   ,				lowercase_      :       Optional[Any]=None   ,				):







    '''simple docstring'''


    lowercase_     =       parent
    lowercase_     =       batch_size
    lowercase_     =       image_size
    lowercase_     =       num_channels
    lowercase_     =       embeddings_size
    lowercase_     =       hidden_sizes
    lowercase_     =       depths
    lowercase_     =       is_training
    lowercase_     =       use_labels
    lowercase_     =       hidden_act
    lowercase_     =       num_labels
    lowercase_     =       scope
    lowercase_     =       len(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    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.num_labels      )

    lowercase_     =       self.get_config()

    return config, pixel_values, labels




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return RegNetConfig(
        num_channels=self.num_channels   ,				embeddings_size=self.embeddings_size   ,				hidden_sizes=self.hidden_sizes   ,				depths=self.depths   ,				hidden_act=self.hidden_act   ,				num_labels=self.num_labels   ,				)




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       List[Any]   ,				lowercase_      :       Tuple   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       RegNetModel(config=lowercase_      )
    model.to(lowercase_      )
    model.eval()
    lowercase_     =       model(lowercase_      )
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape   ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,				)




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       Any   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.num_labels
    lowercase_     =       RegNetForImageClassification(lowercase_      )
    model.to(lowercase_      )
    model.eval()
    lowercase_     =       model(lowercase_   ,				labels=lowercase_      )
    self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)      )





  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    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    (		__a ,				__a ,				unittest.TestCase      ):


  """simple docstring"""

  A_    							=						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  A_    							=						(
      {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  A_    							=						False
  A_    							=						False
  A_    							=						False
  A_    							=						False




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       RegNetModelTester(self      )
    lowercase_     =       ConfigTester(self   ,				config_class=lowercase_   ,				has_text_modality=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return




  @unittest.skip(reason="""RegNet does not use inputs_embeds"""      )
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    pass




  @unittest.skip(reason="""RegNet does not support input and output embeddings"""      )
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    pass




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      lowercase_     =       model_class(lowercase_      )
      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]   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      lowercase_     =       model_class(config=lowercase_      )
      for name, module in model.named_modules():
        if isinstance(lowercase_   ,				(nn.BatchNormad, nn.GroupNorm)      ):
          self.assertTrue(
              torch.all(module.weight == 1      )   ,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,				)
          self.assertTrue(
              torch.all(module.bias == 0      )   ,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,				)




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    def check_hidden_states_output(lowercase_      :       Optional[Any]   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Tuple      ):
      lowercase_     =       model_class(lowercase_      )
      model.to(lowercase_      )
      model.eval()

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

      lowercase_     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      lowercase_     =       self.model_tester.num_stages
      self.assertEqual(len(lowercase_      )   ,				expected_num_stages + 1      )

      # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
      self.assertListEqual(
          list(hidden_states[0].shape[-2:]      )   ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,				)

    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()
    lowercase_     =       ["""basic""", """bottleneck"""]
    for model_class in self.all_model_classes:
      for layer_type in layers_type:
        lowercase_     =       layer_type
        lowercase_     =       True
        check_hidden_states_output(lowercase_   ,				lowercase_   ,				lowercase_      )

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

        check_hidden_states_output(lowercase_   ,				lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowercase_      )





  @slow
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      lowercase_     =       RegNetModel.from_pretrained(lowercase_      )
      self.assertIsNotNone(lowercase_      )






def   A_      (       )			->Optional[Any]:
  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 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    return (
        AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
        if is_vision_available()
        else None
    )





  @slow
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(lowercase_      )

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

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

    # verify the logits
    lowercase_     =       torch.Size((1, 1_000)      )
    self.assertEqual(outputs.logits.shape   ,				lowercase_      )

    lowercase_     =       torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]      ).to(lowercase_      )

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


'''simple docstring'''


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

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  return [
      int(10_00 * (box[0] / width)						),
      int(10_00 * (box[1] / height)						),
      int(10_00 * (box[2] / width)						),
      int(10_00 * (box[3] / height)						),
  ]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  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    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




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







    '''simple docstring'''


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

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    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(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       ImageInput   ,				lowercase_      :       bool = None   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = None   ,				lowercase_      :       bool = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				lowercase_      :       ChannelDimension = ChannelDimension.FIRST   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    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(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    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(lowercase_      )

    if not valid_images(lowercase_      ):
      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.
    lowercase_     =       [to_numpy_array(lowercase_      ) for image in images]

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

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

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

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

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

