

import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class        UpperCamelCase_					(				lowercase_	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   BloomTokenizerFast
    UpperCAmelCase__													=   BloomTokenizerFast
    UpperCAmelCase__													=   True
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   '''tokenizer_file'''
    UpperCAmelCase__													=   {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

           super().setUp()
           A__   				=					BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
           tokenizer.save_pretrained(self.tmpdirname)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				**UpperCAmelCase__   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,				**lowerCamelCase_)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Tuple:





           '''simple docstring'''

           A__   				=					self.get_rust_tokenizer()

           A__   				=					["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           A__   				=					[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

           A__   				=					tokenizer.batch_encode_plus(lowerCamelCase_)["""input_ids"""]
           self.assertListEqual(lowerCamelCase_      ,				lowerCamelCase_)

           A__   				=					tokenizer.batch_decode(lowerCamelCase_)
           self.assertListEqual(lowerCamelCase_      ,				lowerCamelCase_)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Union[str, Any]=6)		->Dict:





           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         A__   				=					self.rust_tokenizer_class.from_pretrained(lowerCamelCase_      ,				**lowerCamelCase_)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         A__   				=					"""This is a simple input"""
                         A__   				=					["""This is a simple input 1""", """This is a simple input 2"""]
                         A__   				=					("""This is a simple input""", """This is a pair""")
                         A__   				=					[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(lowerCamelCase_      ,				max_length=lowerCamelCase_)
                                tokenizer_r.encode_plus(lowerCamelCase_      ,				max_length=lowerCamelCase_)

                                tokenizer_r.batch_encode_plus(lowerCamelCase_      ,				max_length=lowerCamelCase_)
                                tokenizer_r.encode(lowerCamelCase_      ,				max_length=lowerCamelCase_)
                                tokenizer_r.batch_encode_plus(lowerCamelCase_      ,				max_length=lowerCamelCase_)
                         except ValueError:
                                self.fail('''Bloom Tokenizer should be able to deal with padding''')

                         A__   				=					None  # Hotfixing padding = None
                         self.assertRaises(lowerCamelCase_      ,				tokenizer_r.encode      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length''')

                         # Simple input
                         self.assertRaises(lowerCamelCase_      ,				tokenizer_r.encode_plus      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length''')

                         # Simple input
                         self.assertRaises(
                             lowerCamelCase_      ,				tokenizer_r.batch_encode_plus      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length'''      ,				)

                         # Pair input
                         self.assertRaises(lowerCamelCase_      ,				tokenizer_r.encode      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length''')

                         # Pair input
                         self.assertRaises(lowerCamelCase_      ,				tokenizer_r.encode_plus      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length''')

                         # Pair input
                         self.assertRaises(
                             lowerCamelCase_      ,				tokenizer_r.batch_encode_plus      ,				lowerCamelCase_      ,				max_length=lowerCamelCase_      ,				padding='''max_length'''      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					self.get_rust_tokenizer()
           A__   				=					load_dataset('''xnli'''      ,				'''all_languages'''      ,				split='''test'''      ,				streaming=lowerCamelCase_)

           A__   				=					next(iter(lowerCamelCase_))["""premise"""]  # pick up one data
           A__   				=					list(sample_data.values())

           A__   				=					list(map(tokenizer.encode      ,				lowerCamelCase_))
           A__   				=					[tokenizer.decode(lowerCamelCase_      ,				clean_up_tokenization_spaces=lowerCamelCase_) for x in output_tokens]
           self.assertListEqual(lowerCamelCase_      ,				lowerCamelCase_)





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[str]:





           '''simple docstring'''

           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)      ,				1)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])      ,				1)








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

import cva
import numpy as np

# Parrameters
_lowerCamelCase			:	List[str]							 =     (720, 1280)  # Height, Width
_lowerCamelCase			:	Optional[Any]							 =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase			:	List[Any]							 =     1 / 100
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	Union[str, Any]							 =     250







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

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

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

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

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



       """simple docstring"""

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


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


