
"""simple docstring"""




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   import numpy as np
   import torch

if is_vision_available():
   from PIL import Image


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

       lowercase   :    Optional[int]			=							[]
       for i in range(image_processor_tester.batch_size  ):
              if equal_resolution:
                     lowercase   :    Dict			=							image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     lowercase   :    Tuple			=							image_processor_tester.min_resolution
                     if getattr(__snake_case					,			"size_divisor"					,			__snake_case  ):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            lowercase   :    Union[str, Any]			=							max(image_processor_tester.size_divisor					,			__snake_case  )
                     lowercase		,   lowercase   :    Union[str, Any]			=							np.random.choice(np.arange(__snake_case					,			image_processor_tester.max_resolution  )					,			2  )
              image_inputs.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
              lowercase   :    Union[str, Any]			=							[Image.fromarray(np.moveaxis(__snake_case					,			0					,			-1  )  ) for image in image_inputs]

       if torchify:
              lowercase   :    Optional[Any]			=							[torch.from_numpy(__snake_case  ) for image in image_inputs]

       return image_inputs


def   __magic_name__     (				__snake_case    :	Tuple					,			__snake_case    :	str=10					,			__snake_case    :	Union[str, Any]=10					,			__snake_case    :	Union[str, Any]=False					,			__snake_case    :	str=False  )			->    str:
       lowercase   :    Optional[Any]			=							[]
       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
              lowercase   :    Optional[Any]			=							[Image.fromarray(np.moveaxis(__snake_case					,			0					,			-1  )  ) for frame in video]

       if torchify:
              lowercase   :    Optional[Any]			=							[torch.from_numpy(__snake_case  ) for frame in video]

       return video


def   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	List[str]=False					,			__snake_case    :	Tuple=False					,			__snake_case    :	str=False  )			->    Optional[int]:
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

       return video_inputs

class 		a__						:
   __lowerCAmelCase						=    None
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							self.image_processing_class(**self.image_processor_dict    )
          lowercase   :    str			=							json.loads(image_processor.to_json_string()    )
          for key, value in self.image_processor_dict.items():
                 self.assertEqual(obj[key]     ,   _a    )
   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							self.image_processing_class(**self.image_processor_dict    )

          with tempfile.TemporaryDirectory() as tmpdirname:
                 lowercase   :    Any			=							os.path.join(_a     ,   "image_processor.json"    )
                 image_processor_first.to_json_file(_a    )
                 lowercase   :    List[Any]			=							self.image_processing_class.from_json_file(_a    )

          self.assertEqual(image_processor_second.to_dict()     ,   image_processor_first.to_dict()    )
   def       __magic_name__      (      self    ):
          lowercase   :    Dict			=							self.image_processing_class(**self.image_processor_dict    )

          with tempfile.TemporaryDirectory() as tmpdirname:
                 lowercase   :    str			=							image_processor_first.save_pretrained(_a    )[0]
                 check_json_file_has_correct_format(_a    )
                 lowercase   :    List[Any]			=							self.image_processing_class.from_pretrained(_a    )

          self.assertEqual(image_processor_second.to_dict()     ,   image_processor_first.to_dict()    )
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							self.image_processing_class()
          self.assertIsNotNone(_a    )

   @require_torch
   @require_vision
   def       __magic_name__      (      self    ):
          if self.test_cast_dtype is not None:
                 # Initialize image_processor
                 lowercase   :    str			=							self.image_processing_class(**self.image_processor_dict    )

                 # create random PyTorch tensors
                 lowercase   :    str			=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=_a     ,   torchify=_a    )

                 lowercase   :    Optional[int]			=							image_processor(_a     ,   return_tensors="pt"    )
                 # for layoutLM compatiblity
                 self.assertEqual(encoding.pixel_values.device     ,   torch.device("cpu"    )    )
                 self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa    )

                 lowercase   :    List[Any]			=							image_processor(_a     ,   return_tensors="pt"    ).to(torch.floataa    )
                 self.assertEqual(encoding.pixel_values.device     ,   torch.device("cpu"    )    )
                 self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa    )

                 lowercase   :    Union[str, Any]			=							image_processor(_a     ,   return_tensors="pt"    ).to("cpu"     ,   torch.bfloataa    )
                 self.assertEqual(encoding.pixel_values.device     ,   torch.device("cpu"    )    )
                 self.assertEqual(encoding.pixel_values.dtype     ,   torch.bfloataa    )

                 with self.assertRaises(_a    ):
                        lowercase   :    Union[str, Any]			=							image_processor(_a     ,   return_tensors="pt"    ).to(torch.bfloataa     ,   "cpu"    )

                 # Try with text + image feature
                 lowercase   :    Tuple			=							image_processor(_a     ,   return_tensors="pt"    )
                 encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]    )}    )
                 lowercase   :    int			=							encoding.to(torch.floataa    )

                 self.assertEqual(encoding.pixel_values.device     ,   torch.device("cpu"    )    )
                 self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa    )
                 self.assertEqual(encoding.input_ids.dtype     ,   torch.long    )




"""simple docstring"""




import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

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

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							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(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


