


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= LEDTokenizer
    lowercase       			= LEDTokenizerFast
    lowercase       			= True

    def __lowercase       (       self   :  int		)       ->  Tuple:
      super().setUp()
      lowerCAmelCase_      :				Tuple      =       [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      lowerCAmelCase_      :				int      =       dict(zip(lowerCamelCase   ,						range(len(lowerCamelCase		)		)		)		)
      lowerCAmelCase_      :				str      =       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      lowerCAmelCase_      :				List[str]      =       {"""unk_token""": """<unk>"""}

      lowerCAmelCase_      :				str      =       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES["""merges_file"""]		)
      with open(self.vocab_file   ,						"""w"""   ,						encoding="""utf-8"""		) as fp:
        fp.write(json.dumps(lowerCamelCase		) + """\n"""		)
      with open(self.merges_file   ,						"""w"""   ,						encoding="""utf-8"""		) as fp:
        fp.write("""\n""".join(lowerCamelCase		)		)

    def __lowercase       (       self   :  Tuple   ,						**lowerCamelCase   :  Optional[int]		)       ->  int:
      kwargs.update(self.special_tokens_map		)
      return self.tokenizer_class.from_pretrained(self.tmpdirname   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  int   ,						**lowerCamelCase   :  List[str]		)       ->  List[str]:
      kwargs.update(self.special_tokens_map		)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  Any		)       ->  Dict:
      return "lower newer", "lower newer"

    @cached_property
    def __lowercase       (       self   :  Dict		)       ->  int:
      return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""		)

    @cached_property
    def __lowercase       (       self   :  Optional[int]		)       ->  Any:
      return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""		)

    @require_torch
    def __lowercase       (       self   :  Tuple		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Tuple      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      lowerCAmelCase_      :				List[Any]      =       [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				List[str]      =       tokenizer(lowerCamelCase   ,						max_length=len(lowerCamelCase		)   ,						padding=lowerCamelCase   ,						return_tensors="""pt"""		)
        self.assertIsInstance(lowerCamelCase   ,						lowerCamelCase		)

        self.assertEqual((2, 9)   ,						batch.input_ids.shape		)
        self.assertEqual((2, 9)   ,						batch.attention_mask.shape		)
        lowerCAmelCase_      :				Any      =       batch.input_ids.tolist()[0]
        self.assertListEqual(lowerCamelCase   ,						lowerCamelCase		)

    @require_torch
    def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
      lowerCAmelCase_      :				List[Any]      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				Dict      =       tokenizer(lowerCamelCase   ,						padding=lowerCamelCase   ,						return_tensors="""pt"""		)
        self.assertIn("""input_ids"""   ,						lowerCamelCase		)
        self.assertIn("""attention_mask"""   ,						lowerCamelCase		)
        self.assertNotIn("""labels"""   ,						lowerCamelCase		)
        self.assertNotIn("""decoder_attention_mask"""   ,						lowerCamelCase		)

    @require_torch
    def __lowercase       (       self   :  Tuple		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[Any]      =       [
          """Summary of the text.""",
          """Another summary.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				Union[str, Any]      =       tokenizer(text_target=lowerCamelCase   ,						max_length=32   ,						padding="""max_length"""   ,						return_tensors="""pt"""		)
        self.assertEqual(32   ,						targets["""input_ids"""].shape[1]		)

    @require_torch
    def __lowercase       (       self   :  Any		)       ->  str:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				List[Any]      =       tokenizer(
            ["""I am a small frog""" * 10_24, """I am a small frog"""]   ,						padding=lowerCamelCase   ,						truncation=lowerCamelCase   ,						return_tensors="""pt"""		)
        self.assertIsInstance(lowerCamelCase   ,						lowerCamelCase		)
        self.assertEqual(batch.input_ids.shape   ,						(2, 51_22)		)

    @require_torch
    def __lowercase       (       self   :  Any		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       ["""A long paragraph for summarization."""]
      lowerCAmelCase_      :				int      =       [
          """Summary of the text.""",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				Optional[Any]      =       tokenizer(lowerCamelCase   ,						return_tensors="""pt"""		)
        lowerCAmelCase_      :				Any      =       tokenizer(text_target=lowerCamelCase   ,						return_tensors="""pt"""		)
        lowerCAmelCase_      :				Tuple      =       inputs["""input_ids"""]
        lowerCAmelCase_      :				List[Any]      =       targets["""input_ids"""]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)

    @require_torch
    def __lowercase       (       self   :  str		)       ->  Dict:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        lowerCAmelCase_      :				Dict      =       ["""Summary of the text.""", """Another summary."""]
        lowerCAmelCase_      :				List[Any]      =       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        lowerCAmelCase_      :				List[Any]      =       tokenizer(lowerCamelCase   ,						padding=lowerCamelCase		)
        lowerCAmelCase_      :				str      =       [[0] * len(lowerCamelCase		) for x in encoded_output["""input_ids"""]]
        lowerCAmelCase_      :				List[str]      =       tokenizer.pad(lowerCamelCase		)
        self.assertSequenceEqual(outputs["""global_attention_mask"""]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Dict:
      pass





    def __lowercase       (       self   :  List[str]		)       ->  Dict:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
          lowerCAmelCase_      :				Optional[int]      =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase   ,						**lowerCamelCase		)
          lowerCAmelCase_      :				List[Any]      =       self.tokenizer_class.from_pretrained(lowerCamelCase   ,						**lowerCamelCase		)
          lowerCAmelCase_      :				Tuple      =       """A, <mask> AllenNLP sentence."""
          lowerCAmelCase_      :				int      =       tokenizer_r.encode_plus(lowerCamelCase   ,						add_special_tokens=lowerCamelCase   ,						return_token_type_ids=lowerCamelCase		)
          lowerCAmelCase_      :				int      =       tokenizer_p.encode_plus(lowerCamelCase   ,						add_special_tokens=lowerCamelCase   ,						return_token_type_ids=lowerCamelCase		)
          self.assertEqual(sum(tokens_r["""token_type_ids"""]		)   ,						sum(tokens_p["""token_type_ids"""]		)		)
          self.assertEqual(
              sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)   ,						sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)   ,						)

          lowerCAmelCase_      :				str      =       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
          lowerCAmelCase_      :				Dict      =       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
          self.assertSequenceEqual(tokens_p["""input_ids"""]   ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)
          self.assertSequenceEqual(tokens_r["""input_ids"""]   ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)

          self.assertSequenceEqual(
              lowerCamelCase   ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
          self.assertSequenceEqual(
              lowerCamelCase   ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



