

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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def __init__(   self     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  List[Any]=3      ,lowercase__     :  Union[str, Any]=3_2      ,lowercase__     :  str=3      ,lowercase__     :  Any=1_0      ,lowercase__     :  List[Any]=[1_0, 2_0, 3_0, 4_0]      ,lowercase__     :  int=[1, 1, 2, 1]      ,lowercase__     :  Optional[Any]=True      ,lowercase__     :  Any=True      ,lowercase__     :  str="relu"      ,lowercase__     :  Tuple=3      ,lowercase__     :  Union[str, Any]=None      ,):
   __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(lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   __lowercase													=				self.get_config()

   return config, pixel_values






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   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       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[str]    ):
   __lowercase													=				FlaxRegNetModel(config=lowerCamelCase_    )
   __lowercase													=				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 // 3_2, self.image_size // 3_2)      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  str      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				self.num_labels
   __lowercase													=				FlaxRegNetForImageClassification(config=lowerCamelCase_    )
   __lowercase													=				model(lowerCamelCase_    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_flax
class     lowercase_			(a__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  SCREAMING_SNAKE_CASE					:						List[str]           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						List[Any]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				FlaxRegNetModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowerCamelCase_      ,has_text_modality=lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   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       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   pass






  @unittest.skip(reason='''RegNet does not support input and output embeddings'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

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

    __lowercase													=				['''pixel_values''']
    self.assertListEqual(arg_names[:1]      ,lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   def check_hidden_states_output(lowercase__     :  Dict      ,lowercase__     :  Dict      ,lowercase__     :  Optional[Any]    ):
    __lowercase													=				model_class(lowerCamelCase_    )

    __lowercase													=				model(**self._prepare_for_class(lowerCamelCase_      ,lowerCamelCase_    )    )

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

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

   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				True
    check_hidden_states_output(lowerCamelCase_      ,lowerCamelCase_      ,lowerCamelCase_    )

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

    check_hidden_states_output(lowerCamelCase_      ,lowerCamelCase_      ,lowerCamelCase_    )






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

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__    ):
     __lowercase													=				self._prepare_for_class(lowerCamelCase_      ,lowerCamelCase_    )
     __lowercase													=				model_class(lowerCamelCase_    )

     @jax.jit
     def model_jitted(lowercase__     :  Optional[int]      ,**lowercase__     :  Union[str, Any]    ):
      return model(pixel_values=lowerCamelCase_      ,**lowerCamelCase_    )

     with self.subTest('''JIT Enabled'''    ):
      __lowercase													=				model_jitted(**lowerCamelCase_    ).to_tuple()

     with self.subTest('''JIT Disabled'''    ):
      with jax.disable_jit():
       __lowercase													=				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  (					):






 """simple docstring"""



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






@require_flax
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''    ) if is_vision_available() else None






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowerCamelCase_      ,return_tensors='''np'''    )

   __lowercase													=				model(**lowerCamelCase_    )

   # verify the logits
   __lowercase													=				(1, 1_0_0_0)
   self.assertEqual(outputs.logits.shape      ,lowerCamelCase_    )

   __lowercase													=				jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]    )

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




'''simple docstring'''






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				F"{anno[0]} {x_center} {y_center} {width} {height}"
   annos_list.append(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       F"{label_name}.jpg"   )

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) + float(obj[4]   ) / 2

   boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
  if not boxes:
   continue
  img_paths.append(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __lowercase													=				np.zeros([output_size[0], output_size[1], 3]  ,       dtype=np.uinta   )
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				int(scale_x * output_size[1]   )
 __lowercase													=				int(scale_y * output_size[0]   )

 __lowercase													=				[]
 __lowercase													=				[]
 for i, index in enumerate(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   __lowercase													=				cva.resize(A__  ,       (output_size[1] - divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   __lowercase													=				cva.resize(
       A__  ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  __lowercase													=				[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]


def 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


if __name__ == "__main__":
 main()
 print('''DONE ✅''')

