
"""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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor



class __lowerCAmelCase				(					unittest.TestCase      ):




   """simple docstring"""







   def __init__(				self						:					str       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[int]=7       ,   _snake_case						:					Tuple=3       ,   _snake_case						:					List[str]=18       ,   _snake_case						:					Tuple=30       ,   _snake_case						:					List[str]=400       ,   _snake_case						:					int=True       ,   _snake_case						:					int=None       ,   _snake_case						:					Optional[int]=True       ,   _snake_case						:					Optional[Any]=None       ,   _snake_case						:					Any=True       ,   )      -> Any:







        """simple docstring"""
        A_				 =						size if size is not None else {"shortest_edge": 20}
        A_				 =						crop_size if crop_size is not None else {"height": 18, "width": 18}
        A_				 =						parent
        A_				 =						batch_size
        A_				 =						num_channels
        A_				 =						image_size
        A_				 =						min_resolution
        A_				 =						max_resolution
        A_				 =						do_resize
        A_				 =						size
        A_				 =						do_center_crop
        A_				 =						crop_size
        A_				 =						do_flip_channel_order






   def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







        """simple docstring"""
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }





@require_torch
@require_vision
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




   """simple docstring"""







   snake_case	      =				MobileViTImageProcessor if is_vision_available() else None




   def      lowerCamelCase__     (				self						:					Any	)      -> List[str]:







        """simple docstring"""
        A_				 =						MobileViTImageProcessingTester(self	)




   @property
   def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







        """simple docstring"""
        return self.image_processor_tester.prepare_image_processor_dict()




   def      lowerCamelCase__     (				self						:					Dict	)      -> Union[str, Any]:







        """simple docstring"""
        A_				 =						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_flip_channel_order"	)	)




   def      lowerCamelCase__     (				self						:					str	)      -> str:







        """simple docstring"""
        A_				 =						self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size       ,   {"shortest_edge": 20}	)
        self.assertEqual(image_processor.crop_size       ,   {"height": 18, "width": 18}	)

        A_				 =						self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42       ,   crop_size=84	)
        self.assertEqual(image_processor.size       ,   {"shortest_edge": 42}	)
        self.assertEqual(image_processor.crop_size       ,   {"height": 84, "width": 84}	)




   def      lowerCamelCase__     (				self						:					str	)      -> Any:







        """simple docstring"""
        pass




   def      lowerCamelCase__     (				self						:					Any	)      -> Any:







        """simple docstring"""
        A_				 =						self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        A_				 =						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
        A_				 =						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
        A_				 =						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      lowerCamelCase__     (				self						:					List[str]	)      -> List[Any]:







        """simple docstring"""
        A_				 =						self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        A_				 =						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
        A_				 =						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
        A_				 =						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      lowerCamelCase__     (				self						:					Optional[int]	)      -> Union[str, Any]:







        """simple docstring"""
        A_				 =						self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        A_				 =						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
        A_				 =						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
        A_				 =						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"],
            )       ,   )






"""simple docstring"""





import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TransfoXLTokenizer
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[int]:







								"""simple docstring"""
								super().setUp()

								A_				 =						[
								    "<unk>",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "unwanted",
								    "wa",
								    "un",
								    "running",
								    ",",
								    "low",
								    "l",
								]
								A_				 =						os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["vocab_file"]	)
								with open(self.vocab_file       ,   "w"       ,   encoding="utf-8"	) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)




			def      lowerCamelCase__     (				self						:					str       ,   **_snake_case						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Optional[Any]	)      -> Any:







								"""simple docstring"""
								A_				 =						"<unk> UNwanted , running"
								A_				 =						"<unk> unwanted, running"
								return input_text, output_text




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(vocab_file=self.vocab_file       ,   lower_case=_snake_case	)

								A_				 =						tokenizer.tokenize("<unk> UNwanted , running"	)
								self.assertListEqual(_snake_case       ,   ["<unk>", "unwanted", ",", "running"]	)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case	)       ,   [0, 4, 8, 7]	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["hello", "!", "how", "are", "you", "?"]	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["HeLLo", "!", "how", "Are", "yoU", "?"]	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)
								A_				 =						"Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
								A_				 =						[
								    "Hello",
								    "(",
								    "bracket",
								    ")",
								    "and",
								    "side",
								    "@-@",
								    "scrolled",
								    "[",
								    "and",
								    "]",
								    "Henry",
								    "'s",
								    "$",
								    "5",
								    "@,@",
								    "000",
								    "with",
								    "3",
								    "@.@",
								    "34",
								    "m",
								    ".",
								    "What",
								    "'s",
								    "up",
								    "!",
								    "?",
								]

								self.assertListEqual(tokenizer.tokenize(_snake_case	)       ,   _snake_case	)

								self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case	)       ,   _snake_case	)






			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						len(_snake_case	)

								tokenizer.add_tokens(["new1", "new2"]	)
								tokenizer.move_added_token("new1"       ,   1	)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(_snake_case	)       ,   original_len + 2	)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode("new1"	)       ,   [1]	)
								self.assertEqual(tokenizer.decode([1]	)       ,   "new1"	)




