





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY




@is_pipeline_test
class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      UpperCAmelCase_  : Optional[int]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Optional[int]:






        '''simple docstring'''






        if isinstance(model.config			,  UpperCAmelCase				):
          __snake_case      ,				__snake_case				:			str            =    list(tokenizer.lang_code_to_id.keys()				)[:2]
          __snake_case				:			List[str]            =    TranslationPipeline(model=UpperCAmelCase			,  tokenizer=UpperCAmelCase			,  src_lang=UpperCAmelCase			,  tgt_lang=UpperCAmelCase				)
        else:
          __snake_case				:			str            =    TranslationPipeline(model=UpperCAmelCase			,  tokenizer=UpperCAmelCase				)
        return translator, ["Some string", "Some other text"]







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			Tuple            =    translator("Some string"				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": ANY(UpperCAmelCase				)}]				)

        __snake_case				:			str            =    translator(["Some string"]				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": ANY(UpperCAmelCase				)}]				)

        __snake_case				:			List[Any]            =    translator(["Some string", "other string"]				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": ANY(UpperCAmelCase				)}, {"translation_text": ANY(UpperCAmelCase				)}]				)







      @require_torch
      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    pipeline("translation_en_to_ro"			,  model="patrickvonplaten/t5-tiny-random"			,  framework="pt"				)
        __snake_case				:			Dict            =    translator("This is a test string"			,  max_length=20				)
        self.assertEqual(
            UpperCAmelCase			,  [
                {
                    "translation_text": (
                        "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                        " Beide Beide"
                    )
                }
            ]			,  )







      @require_tf
      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case				:			Any            =    pipeline("translation_en_to_ro"			,  model="patrickvonplaten/t5-tiny-random"			,  framework="tf"				)
        __snake_case				:			int            =    translator("This is a test string"			,  max_length=20				)
        self.assertEqual(
            UpperCAmelCase			,  [
                {
                    "translation_text": (
                        "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                        " Beide Beide"
                    )
                }
            ]			,  )







      @require_torch
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			Tuple            =    pipeline("translation_en_to_de"			,  model="patrickvonplaten/t5-tiny-random"			,  framework="pt"				)
        __snake_case				:			Optional[int]            =    translator("This is a test string"			,  max_length=20				)
        self.assertEqual(
            UpperCAmelCase			,  [
                {
                    "translation_text": (
                        "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                        " urine urine urine urine urine urine urine"
                    )
                }
            ]			,  )







      @require_tf
      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        __snake_case				:			Tuple            =    pipeline("translation_en_to_de"			,  model="patrickvonplaten/t5-tiny-random"			,  framework="tf"				)
        __snake_case				:			Optional[Any]            =    translator("This is a test string"			,  max_length=20				)
        self.assertEqual(
            UpperCAmelCase			,  [
                {
                    "translation_text": (
                        "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                        " urine urine urine urine urine urine urine"
                    )
                }
            ]			,  )




class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      @require_torch
      @slow
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        with self.assertRaises(UpperCAmelCase				):
          pipeline(task="translation_cn_to_ar"				)

        # but we do for this one
        __snake_case				:			Optional[Any]            =    pipeline(task="translation_en_to_de"				)
        self.assertEqual(translator._preprocess_params["src_lang"]			,  "en"				)
        self.assertEqual(translator._preprocess_params["tgt_lang"]			,  "de"				)







      @require_torch
      @slow
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case				:			Any            =    MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"				)
        __snake_case				:			Optional[int]            =    MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"				)

        __snake_case				:			List[Any]            =    pipeline(task="translation"			,  model=UpperCAmelCase			,  tokenizer=UpperCAmelCase				)
        # Missing src_lang, tgt_lang
        with self.assertRaises(UpperCAmelCase				):
          translator("This is a test"				)

        __snake_case				:			Tuple            =    translator("This is a test"			,  src_lang="en_XX"			,  tgt_lang="ar_AR"				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": "هذا إختبار"}]				)

        __snake_case				:			Optional[int]            =    translator("This is a test"			,  src_lang="en_XX"			,  tgt_lang="hi_IN"				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": "यह एक परीक्षण है"}]				)

        # src_lang, tgt_lang can be defined at pipeline call time
        __snake_case				:			Optional[Any]            =    pipeline(task="translation"			,  model=UpperCAmelCase			,  tokenizer=UpperCAmelCase			,  src_lang="en_XX"			,  tgt_lang="ar_AR"				)
        __snake_case				:			Optional[Any]            =    translator("This is a test"				)
        self.assertEqual(UpperCAmelCase			,  [{"translation_text": "هذا إختبار"}]				)







      @require_torch
      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    "patrickvonplaten/t5-tiny-random"
        __snake_case				:			int            =    pipeline(task="translation_cn_to_ar"			,  model=UpperCAmelCase				)
        self.assertEqual(translator._preprocess_params["src_lang"]			,  "cn"				)
        self.assertEqual(translator._preprocess_params["tgt_lang"]			,  "ar"				)







      @require_torch
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    "patrickvonplaten/t5-tiny-random"
        with pytest.warns(UpperCAmelCase			,  match=r".*translation_en_to_de.*"				):
          __snake_case				:			Dict            =    pipeline(task="translation"			,  model=UpperCAmelCase				)
        self.assertEqual(translator.task			,  "translation_en_to_de"				)
        self.assertEqual(translator._preprocess_params["src_lang"]			,  "en"				)
        self.assertEqual(translator._preprocess_params["tgt_lang"]			,  "de"				)







      @require_torch
      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        with self.assertRaises(UpperCAmelCase				):
          pipeline(task="translation"				)








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			int            =    0
        # max greyscale value for #FFFFFF
        __snake_case				:			int            =    int(self.get_greyscale(255			,  255			,  255				)				)

        if not self.min_threshold < threshold < self.max_threshold:
          __snake_case				:			Tuple            =    F"""Factor value should be from 0 to {self.max_threshold}"""
          raise ValueError(UpperCAmelCase				)

        __snake_case				:			List[Any]            =    input_img
        __snake_case				:			Any            =    threshold
        __snake_case      ,				__snake_case				:			str            =    self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        __snake_case				:			Dict            =    [
            [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
        ]
        __snake_case				:			str            =    np.ones((self.width, self.height, 3)			,  np.uinta				) * 255







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def   UpperCAmelCase						(	self				)    ->				None:






        '''simple docstring'''






        for y in range(self.height				):
          for x in range(self.width				):
            __snake_case				:			Optional[int]            =    int(self.get_greyscale(*self.input_img[y][x]				)				)
            if self.threshold > greyscale + self.error_table[y][x]:
              __snake_case				:			Any            =    (0, 0, 0)
              __snake_case				:			Optional[int]            =    greyscale + self.error_table[x][y]
            else:
              __snake_case				:			str            =    (255, 255, 255)
              __snake_case				:			Dict            =    greyscale + self.error_table[x][y] - 255
            self.error_table[y][x + 1] += int(8 / 32 * current_error				)
            self.error_table[y][x + 2] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x] += int(8 / 32 * current_error				)
            self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
            self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     _UpperCamelCase    =	[
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 126, 130, 140)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              F'''Original image with dithering threshold: {burkes.threshold}''',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()

