import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      A     :  List[Any]					     =       False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def __init__(							self :       Tuple  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Optional[Any]=7  ,     __magic_name__ :       List[Any]=3  ,     __magic_name__ :       Any=18  ,     __magic_name__ :       Tuple=30  ,     __magic_name__ :       Union[str, Any]=400  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       int=True  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Optional[Any]=None  ,     )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  size if size is not None else {"height": 20, "width": 20}
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  image_size
        SCREAMING_SNAKE_CASE_          =  min_resolution
        SCREAMING_SNAKE_CASE_          =  max_resolution
        SCREAMING_SNAKE_CASE_          =  size
        SCREAMING_SNAKE_CASE_          =  do_normalize
        SCREAMING_SNAKE_CASE_          =  do_convert_rgb
        SCREAMING_SNAKE_CASE_          =  [512, 1_024, 2_048, 4_096]
        SCREAMING_SNAKE_CASE_          =  patch_size if patch_size is not None else {"height": 16, "width": 16}







  def    __A     (							self :       List[str]   )  ->	List[str]:
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__magic_name__  ,     stream=__magic_name__   ).raw   ).convert("RGB"   )
        return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,      )
@require_torch
@require_vision
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						PixaStructImageProcessor if is_vision_available() else None







  def    __A     (							self :       Any   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  PixaStructImageProcessingTester(self   )







  @property
  def    __A     (							self :       str   )  ->	Dict:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_convert_rgb"   )   )







  def    __A     (							self :       List[str]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.prepare_dummy_image()

        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        SCREAMING_SNAKE_CASE_          =  2_048

        SCREAMING_SNAKE_CASE_          =  image_processor(__magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   )
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,     torch.tensor(0.0606   )  ,     atol=1e-3  ,     rtol=1e-3   )   )







  def    __A     (							self :       Optional[int]   )  ->	str:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       List[str]   )  ->	Dict:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        SCREAMING_SNAKE_CASE_          =  True

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              with self.assertRaises(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  image_processor(
                        image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches

              SCREAMING_SNAKE_CASE_          =  "Hello"

              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__  ,     header_text=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__  ,     header_text=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       Tuple   )  ->	str:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     numpify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     np.ndarray   )

        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     torchify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     torch.Tensor   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,      )
@require_torch
@require_vision
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						PixaStructImageProcessor if is_vision_available() else None







  def    __A     (							self :       Any   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  PixaStructImageProcessingTester(self  ,     num_channels=4   )
        SCREAMING_SNAKE_CASE_          =  3







  @property
  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Optional[int]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_convert_rgb"   )   )







  def    __A     (							self :       int   )  ->	List[Any]:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )



from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A     :  Any					     =       [
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A     :  Dict					     =       [
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2", "rougeL"]				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2"]				)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]				).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]				).fmeasure.mean()
      )


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "rougeLsum"
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      assert score > score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  ["rouge1", "rouge2", "rougeL"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      assert score_sep == score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      SCREAMING_SNAKE_CASE_          =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				) == calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      SCREAMING_SNAKE_CASE_          =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]     ,      newline_sep=__UpperCamelCase				)["rougeLsum"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]				)["rougeLsum"]
      assert new_score > prev_score





def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  Path("examples/seq2seq/test_data/wmt_en_ro"				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(
          data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)     ,      bootstrap_aggregation=__UpperCamelCase				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)


