





import unittest

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      str=10		, _lowerCamelCase      :      Optional[Any]=10		, _lowerCamelCase      :      List[Any]=False		, _lowerCamelCase      :      Tuple=False ):
    A__       =    []
    for i in range(image_processor_tester.num_frames ):
        video.append(np.random.randint(2_55		, size=(image_processor_tester.num_channels, width, height)		, dtype=np.uinta ) )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        A__       =    [Image.fromarray(np.moveaxis(_lowerCamelCase		, 0		, -1 ) ) for frame in video]

    if torchify:
        A__       =    [torch.from_numpy(_lowerCamelCase ) for frame in video]

    return video





def 		UpperCamelCase       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      str=False		, _lowerCamelCase      :      List[Any]=False		, _lowerCamelCase      :      Optional[int]=False ):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    A__       =    []
    for i in range(image_processor_tester.batch_size ):
        if equal_resolution:
            A__       =    A__       =    image_processor_tester.max_resolution
        else:
            A__,			A__       =    np.random.choice(
                np.arange(image_processor_tester.min_resolution		, image_processor_tester.max_resolution )		, 2 )
            A__       =    prepare_video(
                image_processor_tester=_lowerCamelCase		, width=_lowerCamelCase		, height=_lowerCamelCase		, numpify=_lowerCamelCase		, torchify=_lowerCamelCase		, )
        video_inputs.append(_lowerCamelCase )

    return video_inputs






class      UpperCAmelCase			( unittest.TestCase       ):
 def __init__(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Tuple=7		, lowercase_   :Optional[Any]=3		, lowercase_   :Optional[Any]=4		, lowercase_   :Any=18		, lowercase_   :Optional[Any]=30		, lowercase_   :Tuple=4_00		, lowercase_   :Tuple=True		, lowercase_   :str=None		, lowercase_   :Dict=True		, lowercase_   :int=[0.5, 0.5, 0.5]		, lowercase_   :Any=[0.5, 0.5, 0.5]		, lowercase_   :int=True		, lowercase_   :Dict=None		, )->							List[Any]:
     A__       =    size if size is not None else {"shortest_edge": 18}
     A__       =    crop_size if crop_size is not None else {"height": 18, "width": 18}

     A__       =    parent
     A__       =    batch_size
     A__       =    num_channels
     A__       =    num_frames
     A__       =    image_size
     A__       =    min_resolution
     A__       =    max_resolution
     A__       =    do_resize
     A__       =    size
     A__       =    do_normalize
     A__       =    image_mean
     A__       =    image_std
     A__       =    do_center_crop
     A__       =    crop_size



 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Dict:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
     }






@require_torch
@require_vision
class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
 __lowercase     	=       TvltImageProcessor if is_vision_available() else None
 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     A__       =    TvltImageProcessorTester(self							)
 @property
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Any:
     return self.image_processor_tester.prepare_image_processor_dict()
 def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(lowercase_		, "image_mean"							)							)
     self.assertTrue(hasattr(lowercase_		, "image_std"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_normalize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_resize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_center_crop"							)							)
     self.assertTrue(hasattr(lowercase_		, "size"							)							)
 def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PIL videos
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, Image.Image							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, numpify=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, np.ndarray							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )



 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Union[str, Any]:
     # Initialize image_processor
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     A__       =    prepare_video_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, torchify=lowercase_							)
     for video in video_inputs:
         self.assertIsInstance(lowercase_		, lowercase_							)
         self.assertIsInstance(video[0]		, torch.Tensor							)

     # Test not batched input
     A__       =    image_processor(video_inputs[0]		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )

     # Test batched
     A__       =    image_processor(lowercase_		, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_videos.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		, )










'''simple docstring'''

import torch

from transformers import AutoTokenizer






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
     A__       =    AutoTokenizer.from_pretrained(lowercase_							)
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:

     if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
         A__       =    None
         for l in x:
             A__       =    self.tokenizer(
                 lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
             A__       =    torch.tensor([len(lowercase_							)]							)
             if d is not None:
                 for k in d.keys():
                     A__       =    torch.cat((d[k], t[k])		, 0							)
             else:
                 A__       =    t

         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
         A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)

     elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
         A__       =    self.tokenizer(
             lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )

     else:
         raise Exception(
             "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
             " strings` for supports are supported."							)

     return d



 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
     A__       =    []
     for idx in range(len(W_query["input_ids"]							)							):
         A__       =    A__       =    range(p_start.shape[1]							)

         A__       =    []
         for start_id in start_indexes:
             for end_id in end_indexes:
                 if start_id < end_id:
                     output.append(
                         (
                             start_id,
                             end_id,
                             p_start[idx][start_id].item(),
                             p_end[idx][end_id].item(),
                         )							)

         output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
         A__       =    []
         for k in range(len(lowercase_							)							):
             if output[k][2] * output[k][3] >= thresh:
                 A__,			A__       =    output[k][0], output[k][1]
                 A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                 temp.append((decoded, output[k][2] * output[k][3])							)

         final_outputs.append(lowercase_							)

     return final_outputs



