






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				_UpperCamelCase      (a_				,		unittest.TestCase      ):
      snake_case_	=				BarthezTokenizer
      snake_case_	=				BarthezTokenizerFast
      snake_case_	=				True
      snake_case_	=				True


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           super().setUp()

           __lowerCAmelCase             =       BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"						)
           tokenizer.save_pretrained(self.tmpdirname						)
           tokenizer.save_pretrained(self.tmpdirname       ,    legacy_format=__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       "<pad>"
           __lowerCAmelCase             =       1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase						)       ,    __UpperCamelCase						)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase						)       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       list(self.get_tokenizer().get_vocab().keys()						)

           self.assertEqual(vocab_keys[0]       ,    "<s>"						)
           self.assertEqual(vocab_keys[1]       ,    "<pad>"						)
           self.assertEqual(vocab_keys[-1]       ,    "<mask>"						)
           self.assertEqual(len(__UpperCamelCase						)       ,    1_0_1_1_2_2						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           self.assertEqual(self.get_tokenizer().vocab_size       ,    1_0_1_1_2_2						)


      @require_torch
      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
           __lowerCAmelCase             =       [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

           __lowerCAmelCase             =       self.tokenizer(
               __UpperCamelCase       ,    max_length=len(__UpperCamelCase						)       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    return_tensors="pt"						)
           self.assertIsInstance(__UpperCamelCase       ,    __UpperCamelCase						)

           self.assertEqual((2, 6)       ,    batch.input_ids.shape						)
           self.assertEqual((2, 6)       ,    batch.attention_mask.shape						)
           __lowerCAmelCase             =       batch.input_ids.tolist()[0]
           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           if not self.test_rust_tokenizer:
                return

           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_rust_tokenizer()

           __lowerCAmelCase             =       "I was born in 92000, and this is falsé."

           __lowerCAmelCase             =       tokenizer.tokenize(__UpperCamelCase						)
           __lowerCAmelCase             =       rust_tokenizer.tokenize(__UpperCamelCase						)
           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer.encode(__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase						)
           __lowerCAmelCase             =       rust_tokenizer.encode(__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase						)
           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       self.get_rust_tokenizer()
           __lowerCAmelCase             =       tokenizer.encode(__UpperCamelCase						)
           __lowerCAmelCase             =       rust_tokenizer.encode(__UpperCamelCase						)
           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           # fmt: off
           __lowerCAmelCase             =       {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # moussaKam/mbarthez is a french model. So we also use french texts.
           __lowerCAmelCase             =       [
               "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
               "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
               "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
               "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
               "telles que la traduction et la synthèse de texte.",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=__UpperCamelCase       ,    model_name="moussaKam/mbarthez"       ,    revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,    sequences=__UpperCamelCase       ,    )










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

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE__     					=			(720, 1_280)  # Height, Width
SCREAMING_SNAKE_CASE__     					=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__     					=			1 / 100
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			250


def     lowercase	(    ):



		'''simple docstring'''




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

				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				SCREAMING_SNAKE_CASE_				:int				     =						random_chars(32 )
				SCREAMING_SNAKE_CASE_				:Dict				     =						path.split(os.sep )[-1].rsplit("."     , 1 )[0]
				SCREAMING_SNAKE_CASE_				:Dict				     =						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}" )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
				for anno in new_annos:
						SCREAMING_SNAKE_CASE_				:Any				     =						anno[3] - anno[1]
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						anno[4] - anno[2]
						SCREAMING_SNAKE_CASE_				:Any				     =						anno[1] + width / 2
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						anno[2] + height / 2
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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 ):



		'''simple docstring'''




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

				SCREAMING_SNAKE_CASE_				:Dict				     =						[]
				for obj_list in obj_lists:
						SCREAMING_SNAKE_CASE_				:Dict				     =						obj_list.rstrip("\n" ).split(" " )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						float(obj[1] ) - float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:Dict				     =						float(obj[2] ) - float(obj[4] ) / 2
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(obj[1] ) + float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:List[str]				     =						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     , ):



		'''simple docstring'''




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

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



		'''simple docstring'''




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


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






