





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil


@pytest.fixture
def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Optional[int]           =						tmp_path / 'text.txt'
	snake_case_		:      Union[str, Any]           =						textwrap.dedent(
	    '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        ' )
	with open(__a ,			'w' ,			encoding='utf-8' ) as f:
		f.write(__a )
	return str(__a )


@pytest.fixture
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Dict           =						tmp_path / 'text_with_image.txt'
	with open(__a ,			'w' ,			encoding='utf-8' ) as f:
		f.write(__a )
	return str(__a )


@pytest.mark.parametrize('keep_linebreaks' ,			[True, False] )
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      Any           =						f.read().splitlines(keepends=__a )
	snake_case_		:      str           =						Text(keep_linebreaks=__a ,			encoding='utf-8' )
	snake_case_		:      Optional[int]           =						text._generate_tables([[text_file]] )
	snake_case_		:      Dict           =						pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
	assert generated_content == expected_content


@require_pil
def 				SCREAMING_SNAKE_CASE__    (     __a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      Dict           =						f.read().splitlines()[0]
	snake_case_		:      Any           =						Text(encoding='utf-8' ,			features=Features({'image': Image()} ) )
	snake_case_		:      Union[str, Any]           =						text._generate_tables([[text_file_with_image]] )
	snake_case_		:      List[str]           =						pa.concat_tables([table for _, table in generator] )
	assert pa_table.schema.field('image' ).type == Image()()
	snake_case_		:      int           =						pa_table.to_pydict()['image']
	assert generated_content == [{"path": image_file, "bytes": None}]


@pytest.mark.parametrize('sample_by' ,			['line', 'paragraph', 'document'] )
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      str           =						f.read()
	if sample_by == "line":
		snake_case_		:      Dict           =						expected_content.splitlines()
	elif sample_by == "paragraph":
		snake_case_		:      Optional[int]           =						expected_content.split('\n\n' )
	elif sample_by == "document":
		snake_case_		:      Optional[Any]           =						[expected_content]
	snake_case_		:      List[Any]           =						Text(sample_by=__a ,			encoding='utf-8' ,			chunksize=1_00 )
	snake_case_		:      int           =						text._generate_tables([[text_file]] )
	snake_case_		:      List[str]           =						pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
	assert generated_content == expected_content





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			,  __lowerCamelCase=10			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   []
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255			,  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
  snake_case :			Optional[int]							=   [Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for frame in video]

 if torchify:
  snake_case :			int							=   [torch.from_numpy(__lowerCamelCase							) for frame in video]

 return video






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 snake_case :			Any							=   []
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   snake_case :			Optional[int]							=   image_processor_tester.max_resolution
  else:
   snake_case	,       snake_case :			str							=   np.random.choice(
       np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution							)			,  2							)
   snake_case :			Any							=   prepare_video(
       image_processor_tester=__lowerCamelCase			,  width=__lowerCamelCase			,  height=__lowerCamelCase			,  numpify=__lowerCamelCase			,  torchify=__lowerCamelCase			,  )
  video_inputs.append(__lowerCamelCase							)

 return video_inputs

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=7						,    a__=3						,    a__=4						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    a__=True						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Tuple							=   size if size is not None else {'''shortest_edge''': 18}
        snake_case :			List[str]							=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        snake_case :			Optional[Any]							=   parent
        snake_case :			List[Any]							=   batch_size
        snake_case :			Optional[int]							=   num_channels
        snake_case :			Union[str, Any]							=   num_frames
        snake_case :			List[Any]							=   image_size
        snake_case :			Tuple							=   min_resolution
        snake_case :			List[Any]							=   max_resolution
        snake_case :			Tuple							=   do_resize
        snake_case :			List[Any]							=   size
        snake_case :			List[Any]							=   do_normalize
        snake_case :			List[Any]							=   image_mean
        snake_case :			List[str]							=   image_std
        snake_case :			Tuple							=   do_center_crop
        snake_case :			str							=   crop_size



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        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   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   TvltImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   TvltImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(a__						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(a__						,    '''image_std'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_center_crop'''       )       )
        self.assertTrue(hasattr(a__						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL videos
        snake_case :			List[str]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   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
        snake_case :			Optional[Any]							=   image_processor(a__						,    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       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Union[str, Any]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    numpify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    np.ndarray       )

        # Test not batched input
        snake_case :			List[str]							=   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
        snake_case :			Tuple							=   image_processor(a__						,    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       ):






        '''simple docstring'''






        snake_case :			str							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			str							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    torchify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Tuple							=   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
        snake_case :			Optional[int]							=   image_processor(a__						,    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'''],
            )						,    )






