





'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





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

            '''simple docstring'''




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





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

            '''simple docstring'''




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

            _UpperCamelCase							       =			self.get_config()

            return config, pixel_values





       def snake_case__		(				self  :						str	)       ->	Any:

            '''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			,					image_size=self.image_size			,					)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModel(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,					)





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			FlaxRegNetForImageClassification(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)	)







       def snake_case__		(				self  :						int	)       ->	Any:

            '''simple docstring'''




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

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       _snake_case      :	Any									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						str	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Union[str, 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 snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)





       @unittest.skip(reason='''RegNet does not use inputs_embeds'''	)
       def snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''RegNet does not support input and output embeddings'''	)
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]	):
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                 _UpperCamelCase							       =			model(**self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)	)

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

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

            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			True
                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

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

                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)







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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def model_jitted(lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	):
                           return model(pixel_values=lowerCAmelCase__			,					**lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

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







def    a__							(							)						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''	) if is_vision_available() else None







       @slow
       def snake_case__		(				self  :						Tuple	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	)

            _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            # verify the logits
            _UpperCamelCase							       =			(1, 1000)
            self.assertEqual(outputs.logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			jnp.array([-0.4180, -1.5051, -3.4836]	)

            self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,					lowerCAmelCase__			,					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

