


"""simple docstring"""







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

import cva
import numpy as np

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


def      lowercase			(				)->      None:
  '''simple docstring'''







  a							,		a						:		Optional[Any]      							=			get_dataset(A_  ,   A_       )
  for index in range(A_       ):
    a						:		Optional[int]      							=			random.sample(range(len(A_       )       )  ,   4       )
    a							,		a							,		a						:		str      							=			update_image_and_anno(
        A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   filter_scale=A_  ,   )

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    a						:		str      							=			random_chars(32       )
    a						:		Optional[Any]      							=			path.split(os.sep       )[-1].rsplit("."  ,   1       )[0]
    a						:		Union[str, Any]      							=			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}'''       )
    a						:		str      							=			[]
    for anno in new_annos:
      a						:		Union[str, Any]      							=			anno[3] - anno[1]
      a						:		Any      							=			anno[4] - anno[2]
      a						:		Any      							=			anno[1] + width / 2
      a						:		str      							=			anno[2] + height / 2
      a						:		Optional[Any]      							=			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      lowercase			(				A_  ,   A_       )->      tuple[list, list]:
  '''simple docstring'''







  a						:		Any      							=			[]
  a						:		Dict      							=			[]
  for label_file in glob.glob(os.path.join(A_  ,   "*.txt"       )       ):
    a						:		Union[str, Any]      							=			label_file.split(os.sep       )[-1].rsplit("."  ,   1       )[0]
    with open(A_       ) as in_file:
      a						:		Union[str, Any]      							=			in_file.readlines()
    a						:		Tuple      							=			os.path.join(A_  ,   F'''{label_name}.jpg'''       )

    a						:		Optional[Any]      							=			[]
    for obj_list in obj_lists:
      a						:		str      							=			obj_list.rstrip("\n"       ).split(" "       )
      a						:		str      							=			float(obj[1]       ) - float(obj[3]       ) / 2
      a						:		Optional[Any]      							=			float(obj[2]       ) - float(obj[4]       ) / 2
      a						:		Optional[int]      							=			float(obj[1]       ) + float(obj[3]       ) / 2
      a						:		Optional[Any]      							=			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      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_ = 0.0  ,   )->      tuple[list, list, str]:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			np.zeros([output_size[0], output_size[1], 3]  ,   dtype=np.uinta       )
  a						:		Dict      							=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						:		List[Any]      							=			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  a						:		Union[str, Any]      							=			int(scale_x * output_size[1]       )
  a						:		Tuple      							=			int(scale_y * output_size[0]       )

  a						:		List[Any]      							=			[]
  a						:		int      							=			[]
  for i, index in enumerate(A_       ):
    a						:		Union[str, Any]      							=			all_img_list[index]
    path_list.append(A_       )
    a						:		List[str]      							=			all_annos[index]
    a						:		Union[str, Any]      							=			cva.imread(A_       )
    if i == 0:  # top-left
      a						:		Any      							=			cva.resize(A_  ,   (divid_point_x, divid_point_y)       )
      a						:		Tuple      							=			img
      for bbox in img_annos:
        a						:		List[Any]      							=			bbox[1] * scale_x
        a						:		Dict      							=			bbox[2] * scale_y
        a						:		List[str]      							=			bbox[3] * scale_x
        a						:		Any      							=			bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    elif i == 1:  # top-right
      a						:		Any      							=			cva.resize(A_  ,   (output_size[1] - divid_point_x, divid_point_y)       )
      a						:		int      							=			img
      for bbox in img_annos:
        a						:		List[Any]      							=			scale_x + bbox[1] * (1 - scale_x)
        a						:		int      							=			bbox[2] * scale_y
        a						:		List[str]      							=			scale_x + bbox[3] * (1 - scale_x)
        a						:		Union[str, Any]      							=			bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    elif i == 2:  # bottom-left
      a						:		Optional[Any]      							=			cva.resize(A_  ,   (divid_point_x, output_size[0] - divid_point_y)       )
      a						:		Optional[int]      							=			img
      for bbox in img_annos:
        a						:		Any      							=			bbox[1] * scale_x
        a						:		Tuple      							=			scale_y + bbox[2] * (1 - scale_y)
        a						:		List[Any]      							=			bbox[3] * scale_x
        a						:		Optional[Any]      							=			scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
    else:  # bottom-right
      a						:		int      							=			cva.resize(
          A_  ,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)       )
      a						:		List[Any]      							=			img
      for bbox in img_annos:
        a						:		Tuple      							=			scale_x + bbox[1] * (1 - scale_x)
        a						:		Dict      							=			scale_y + bbox[2] * (1 - scale_y)
        a						:		Tuple      							=			scale_x + bbox[3] * (1 - scale_x)
        a						:		List[str]      							=			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:
    a						:		Dict      							=			[
        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      lowercase			(				A_       )->      str:
  '''simple docstring'''







  assert number_char > 1, "The number of character should greater than 1"
  a						:		Optional[int]      							=			ascii_lowercase + digits
  return "".join(random.choice(A_       ) for _ in range(A_       )       )


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










"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[Any]=1_3    , UpperCamelCase_     :    Tuple=7    , UpperCamelCase_     :    List[Any]=True    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    Optional[Any]=True    , UpperCamelCase_     :    List[str]=9_9    , UpperCamelCase_     :    str=3_2    , UpperCamelCase_     :    Union[str, Any]=5    , UpperCamelCase_     :    int=4    , UpperCamelCase_     :    Optional[Any]=3_7    , UpperCamelCase_     :    Optional[int]="gelu"    , UpperCamelCase_     :    Any=0.1    , UpperCamelCase_     :    List[str]=0.1    , UpperCamelCase_     :    str=5_1_2    , UpperCamelCase_     :    Optional[Any]=1_6    , UpperCamelCase_     :    Union[str, Any]=2    , UpperCamelCase_     :    Any=0.02    , UpperCamelCase_     :    Union[str, Any]=4    , ):
           lowerCAmelCase    :		str				=      parent
           lowerCAmelCase    :		List[str]				=      batch_size
           lowerCAmelCase    :		int				=      seq_length
           lowerCAmelCase    :		str				=      is_training
           lowerCAmelCase    :		Tuple				=      use_attention_mask
           lowerCAmelCase    :		Dict				=      use_token_type_ids
           lowerCAmelCase    :		Optional[int]				=      use_labels
           lowerCAmelCase    :		Optional[Any]				=      vocab_size
           lowerCAmelCase    :		Optional[int]				=      hidden_size
           lowerCAmelCase    :		Optional[Any]				=      num_hidden_layers
           lowerCAmelCase    :		str				=      num_attention_heads
           lowerCAmelCase    :		Optional[Any]				=      intermediate_size
           lowerCAmelCase    :		int				=      hidden_act
           lowerCAmelCase    :		int				=      hidden_dropout_prob
           lowerCAmelCase    :		Tuple				=      attention_probs_dropout_prob
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		str				=      type_vocab_size
           lowerCAmelCase    :		str				=      type_sequence_label_size
           lowerCAmelCase    :		Any				=      initializer_range
           lowerCAmelCase    :		int				=      num_choices






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Tuple				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		Optional[int]				=      None
           if self.use_attention_mask:
                 lowerCAmelCase    :		Union[str, Any]				=      random_attention_mask([self.batch_size, self.seq_length] )

           lowerCAmelCase    :		Union[str, Any]				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		Dict				=      ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

           lowerCAmelCase    :		Union[str, Any]				=      RobertaPreLayerNormConfig(
               vocab_size=self.vocab_size    , 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    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , is_decoder=UpperCamelCase_    , initializer_range=self.initializer_range    , )

           return config, input_ids, token_type_ids, attention_mask






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		List[str]				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      config_and_inputs
           lowerCAmelCase    :		Optional[Any]				=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		int				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      config_and_inputs

           lowerCAmelCase    :		str				=      True
           lowerCAmelCase    :		Optional[Any]				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
           lowerCAmelCase    :		str				=      ids_tensor([self.batch_size, self.seq_length]    , vocab_size=2 )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    True

     __UpperCamelCase     =    (
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Any				=      FlaxRobertaPreLayerNormModelTester(self )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase    :		Optional[int]				=      model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      model(np.ones((1, 1) ) )
                 self.assertIsNotNone(UpperCamelCase_ )



@require_flax
class 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		str				=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		Union[str, Any]				=      model(UpperCamelCase_ )[0]
           lowerCAmelCase    :		str				=      [1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape )    , UpperCamelCase_ )
           # compare the actual values for a slice.
           lowerCAmelCase    :		Optional[Any]				=      np.array(
               [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Dict				=      FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		str				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		str				=      model(UpperCamelCase_ )[0]
           # compare the actual values for a slice.
           lowerCAmelCase    :		str				=      np.array(
               [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )


