



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class       _lowerCAmelCase (			_UpperCAmelCase     ,       unittest.TestCase      ):






			"""simple docstring"""





			lowercase__   :	List[str]  			=      HerbertTokenizer
			lowercase__   :	Union[str, Any]  			=      HerbertTokenizerFast
			lowercase__   :	Union[str, Any]  			=      True





			def 		snake_case__      (					self						:							List[str] )    ->       int:
									"""simple docstring"""







									super().setUp()

									# Use a simpler test file without japanese/chinese characters
									with open(F"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"      ,  encoding="""utf-8""" ) as f_data:
															__lowercase											=					f_data.read().replace("""\n\n"""      ,  """\n""" ).strip()

									__lowercase											=					[
									    """<s>""",
									    """</s>""",
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """,</w>""",
									    """<unk>""",
									]
									__lowercase											=					dict(zip(_UpperCAmelCase      ,  range(len(_UpperCAmelCase ) ) ) )
									__lowercase											=					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

									__lowercase											=					os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES["""vocab_file"""] )
									__lowercase											=					os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES["""merges_file"""] )
									with open(self.vocab_file      ,  """w""" ) as fp:
															fp.write(json.dumps(_UpperCAmelCase ) )
									with open(self.merges_file      ,  """w""" ) as fp:
															fp.write("""\n""".join(_UpperCAmelCase ) )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							Optional[Any] )    ->       Optional[Any]:
									"""simple docstring"""







									__lowercase											=					"""lower newer"""
									__lowercase											=					"""lower newer"""
									return input_text, output_text





			def 		snake_case__      (					self						:							Tuple )    ->       Any:
									"""simple docstring"""







									__lowercase											=					self.tokenizer_class(vocab_file=self.vocab_file      ,  merges_file=self.merges_file )

									__lowercase											=					"""lower"""
									__lowercase											=					["""low""", """er</w>"""]
									__lowercase											=					tokenizer.tokenize(_UpperCAmelCase )
									self.assertListEqual(_UpperCAmelCase      ,  _UpperCAmelCase )

									__lowercase											=					tokens + ["""<unk>"""]
									__lowercase											=					[16, 17, 23]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase )      ,  _UpperCAmelCase )





			def 		snake_case__      (					self						:							Optional[int] )    ->       Union[str, Any]:
									"""simple docstring"""







									if not self.test_rust_tokenizer:
															return

									__lowercase											=					self.get_tokenizer()
									__lowercase											=					self.get_rust_tokenizer()

									__lowercase											=					"""lower,newer"""

									__lowercase											=					tokenizer.tokenize(_UpperCAmelCase )
									__lowercase											=					rust_tokenizer.tokenize(_UpperCAmelCase )
									self.assertListEqual(_UpperCAmelCase      ,  _UpperCAmelCase )

									__lowercase											=					tokenizer.encode(_UpperCAmelCase      ,  add_special_tokens=_UpperCAmelCase )
									__lowercase											=					rust_tokenizer.encode(_UpperCAmelCase      ,  add_special_tokens=_UpperCAmelCase )
									self.assertListEqual(_UpperCAmelCase      ,  _UpperCAmelCase )

									__lowercase											=					self.get_rust_tokenizer()
									__lowercase											=					tokenizer.encode(_UpperCAmelCase )
									__lowercase											=					rust_tokenizer.encode(_UpperCAmelCase )
									self.assertListEqual(_UpperCAmelCase      ,  _UpperCAmelCase )







			@slow
			def 		snake_case__      (					self						:							Optional[Any] )    ->       Any:
									"""simple docstring"""







									__lowercase											=					self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased""" )

									__lowercase											=					tokenizer.encode("""konstruowanie sekwencji"""      ,  add_special_tokens=_UpperCAmelCase )
									__lowercase											=					tokenizer.encode("""konstruowanie wielu sekwencji"""      ,  add_special_tokens=_UpperCAmelCase )

									__lowercase											=					tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
									__lowercase											=					tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase      ,  _UpperCAmelCase )

									assert encoded_sentence == [0] + text + [2]
									assert encoded_pair == [0] + text + [2] + text_a + [2]













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 SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )