






'''simple docstring'''


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 							lowerCAmelCase					(							lowerCAmelCase_							,      unittest.TestCase   ):







   """simple docstring"""







   _A						=   BertweetTokenizer
   _A						=   False



   def    __magic_name__    ( self			)    ->   List[str]:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __a    :	List[str]									=						['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
    __a    :	Dict									=						dict(zip(__SCREAMING_SNAKE_CASE   ,   range(len(__SCREAMING_SNAKE_CASE			)			)			)			)
    __a    :	Optional[Any]									=						['#version: 0.2', 'a m</w>']
    __a    :	Optional[int]									=						{'unk_token': '<unk>'}

    __a    :	Any									=						os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['vocab_file']			)
    __a    :	int									=						os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['merges_file']			)
    with open(self.vocab_file   ,   'w'   ,   encoding='utf-8'			) as fp:
     for token in vocab_tokens:
      fp.write(f'''{token} {vocab_tokens[token]}\n'''			)
    with open(self.merges_file   ,   'w'   ,   encoding='utf-8'			) as fp:
     fp.write('\n'.join(__SCREAMING_SNAKE_CASE			)			)



   def    __magic_name__    ( self   ,   **_A			)    ->   Optional[Any]:
    kwargs.update(self.special_tokens_map			)
    return BertweetTokenizer.from_pretrained(self.tmpdirname   ,   **__SCREAMING_SNAKE_CASE			)



   def    __magic_name__    ( self   ,   _A			)    ->   Union[str, Any]:
    __a    :	str									=						'I am VinAI Research'
    __a    :	Optional[int]									=						'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
    return input_text, output_text



   def    __magic_name__    ( self			)    ->   List[Any]:
    __a    :	str									=						BertweetTokenizer(self.vocab_file   ,   self.merges_file   ,   **self.special_tokens_map			)
    __a    :	List[str]									=						'I am VinAI Research'
    __a    :	List[Any]									=						'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
    __a    :	Optional[int]									=						tokenizer.tokenize(__SCREAMING_SNAKE_CASE			)
    self.assertListEqual(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE			)

    __a    :	Dict									=						tokens + [tokenizer.unk_token]

    __a    :	Optional[int]									=						[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			)   ,   __SCREAMING_SNAKE_CASE			)












'''simple docstring'''


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   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=10       ,		SCREAMING_SNAKE_CASE__=10       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False		):
 __a    :	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
  __a    :	List[Any]									=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,		0       ,		-1		)		) for frame in video]

 if torchify:
  __a    :	List[Any]									=						[torch.from_numpy(SCREAMING_SNAKE_CASE__		) for frame in video]

 return video






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False		):
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

 return video_inputs




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A=7   ,   _A=3   ,   _A=4   ,   _A=18   ,   _A=30   ,   _A=400   ,   _A=True   ,   _A=None   ,   _A=True   ,   _A=[0.5, 0.5, 0.5]   ,   _A=[0.5, 0.5, 0.5]   ,   _A=True   ,   _A=None   ,   )    ->   str:
    __a    :	Any									=						size if size is not None else {'shortest_edge': 18}
    __a    :	str									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}

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



   def    __magic_name__    ( self			)    ->   Any:
    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					(							lowerCamelCase__							,      unittest.TestCase   ):







   """simple docstring"""







   _A						=   TvltImageProcessor if is_vision_available() else None



   def    __magic_name__    ( self			)    ->   int:
    __a    :	List[Any]									=						TvltImageProcessorTester(self			)



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



   def    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	Any									=						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    __magic_name__    ( self			)    ->   str:
    # Initialize image_processor
    __a    :	str									=						self.image_processing_class(**self.image_processor_dict			)
    # create random PIL videos
    __a    :	Optional[Any]									=						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
    __a    :	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
    __a    :	Dict									=						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    __magic_name__    ( self			)    ->   Any:
    # Initialize image_processor
    __a    :	Union[str, Any]									=						self.image_processing_class(**self.image_processor_dict			)
    # create random numpy tensors
    __a    :	List[str]									=						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
    __a    :	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
    __a    :	Dict									=						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    __magic_name__    ( self			)    ->   Union[str, Any]:
    # Initialize image_processor
    __a    :	Optional[Any]									=						self.image_processing_class(**self.image_processor_dict			)
    # create random PyTorch tensors
    __a    :	Optional[Any]									=						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
    __a    :	Dict									=						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    :	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'],
        )   ,   )




