



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, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor




class  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def __init__(	self  :  List[str],			_snake_case  :  List[str],			_snake_case  :  Optional[Any]=7,			_snake_case  :  Dict=3,			_snake_case  :  Dict=1_8,			_snake_case  :  List[str]=3_0,			_snake_case  :  Optional[int]=4_0_0,			_snake_case  :  int=True,			_snake_case  :  Tuple=None,			_snake_case  :  Dict=True,			_snake_case  :  Tuple=None,			_snake_case  :  List[Any]=True,			_snake_case  :  Optional[int]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],			_snake_case  :  Optional[int]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],			_snake_case  :  Tuple=True,			)       ->List[str]:
            snake_case__							:     List[str]            =     parent
            snake_case__							:     Optional[int]            =     batch_size
            snake_case__							:     Optional[Any]            =     num_channels
            snake_case__							:     Any            =     image_size
            snake_case__							:     Union[str, Any]            =     min_resolution
            snake_case__							:     str            =     max_resolution
            snake_case__							:     int            =     do_resize
            snake_case__							:     Optional[int]            =     size if size is not None else {'height': 1_8, 'width': 1_8}
            snake_case__							:     List[Any]            =     do_center_crop
            snake_case__							:     Tuple            =     crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
            snake_case__							:     str            =     do_normalize
            snake_case__							:     Union[str, Any]            =     image_mean
            snake_case__							:     List[str]            =     image_std
            snake_case__							:     List[str]            =     do_convert_rgb






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




@require_torch
@require_vision
class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     OwlViTImageProcessor if is_vision_available() else None






     def        lowercase_       (	self  :  Tuple )       ->Optional[Any]:
            snake_case__							:     Union[str, Any]            =     OwlViTImageProcessingTester(self )






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






     def        lowercase_       (	self  :  Dict )       ->List[str]:
            snake_case__							:     Tuple            =     self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(_snake_case,			'do_resize' ) )
            self.assertTrue(hasattr(_snake_case,			'size' ) )
            self.assertTrue(hasattr(_snake_case,			'do_center_crop' ) )
            self.assertTrue(hasattr(_snake_case,			'center_crop' ) )
            self.assertTrue(hasattr(_snake_case,			'do_normalize' ) )
            self.assertTrue(hasattr(_snake_case,			'image_mean' ) )
            self.assertTrue(hasattr(_snake_case,			'image_std' ) )
            self.assertTrue(hasattr(_snake_case,			'do_convert_rgb' ) )






     def        lowercase_       (	self  :  int )       ->Dict:
            snake_case__							:     int            =     self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size,			{'height': 1_8, 'width': 1_8} )
            self.assertEqual(image_processor.crop_size,			{'height': 1_8, 'width': 1_8} )

            snake_case__							:     List[Any]            =     self.image_processing_class.from_dict(self.image_processor_dict,			size=4_2,			crop_size=8_4 )
            self.assertEqual(image_processor.size,			{'height': 4_2, 'width': 4_2} )
            self.assertEqual(image_processor.crop_size,			{'height': 8_4, 'width': 8_4} )






     def        lowercase_       (	self  :  Dict )       ->Any:
            # Initialize image_processing
            snake_case__							:     Tuple            =     self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            snake_case__							:     Any            =     prepare_image_inputs(self.image_processor_tester,			equal_resolution=_snake_case )

            for image in image_inputs:
                   self.assertIsInstance(_snake_case,			Image.Image )

            # Test not batched input
            snake_case__							:     Union[str, Any]            =     image_processing(image_inputs[0],			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    1,
                    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_processing(_snake_case,			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),			)






     def        lowercase_       (	self  :  Optional[Any] )       ->Union[str, Any]:
            # Initialize image_processing
            snake_case__							:     Optional[Any]            =     self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            snake_case__							:     List[Any]            =     prepare_image_inputs(self.image_processor_tester,			equal_resolution=_snake_case,			numpify=_snake_case )
            for image in image_inputs:
                   self.assertIsInstance(_snake_case,			np.ndarray )

            # Test not batched input
            snake_case__							:     Optional[int]            =     image_processing(image_inputs[0],			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    1,
                    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_processing(_snake_case,			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),			)






     def        lowercase_       (	self  :  List[str] )       ->Dict:
            # Initialize image_processing
            snake_case__							:     Optional[Any]            =     self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            snake_case__							:     Optional[Any]            =     prepare_image_inputs(self.image_processor_tester,			equal_resolution=_snake_case,			torchify=_snake_case )
            for image in image_inputs:
                   self.assertIsInstance(_snake_case,			torch.Tensor )

            # Test not batched input
            snake_case__							:     List[Any]            =     image_processing(image_inputs[0],			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    1,
                    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_processing(_snake_case,			return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_images.shape,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),			)











import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     BertweetTokenizer
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            super().setUp()

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

            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     Tuple            =     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(_snake_case ) )






     def        lowercase_       (	self  :  List[Any],			**_snake_case  :  List[Any] )       ->Optional[Any]:
            kwargs.update(self.special_tokens_map )
            return BertweetTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Tuple )       ->Union[str, Any]:
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
            return input_text, output_text






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

            snake_case__							:     Tuple            =     tokens + [tokenizer.unk_token]

            snake_case__							:     Dict            =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ),			_snake_case )






