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 ViltImageProcessor






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=7				,  lowerCAmelCase_=3				,  lowerCAmelCase_=18				,  lowerCAmelCase_=30				,  lowerCAmelCase_=4_00				,  lowerCAmelCase_=True				,  lowerCAmelCase_=None				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=[0.5, 0.5, 0.5]				,  lowerCAmelCase_=[0.5, 0.5, 0.5]				,  )						->Tuple:
        a	:     Dict						      =    size if size is not None else {'''shortest_edge''': 30}
        a	:     Union[str, Any]						      =    parent
        a	:     Any						      =    batch_size
        a	:     Any						      =    num_channels
        a	:     List[Any]						      =    image_size
        a	:     List[Any]						      =    min_resolution
        a	:     Dict						      =    max_resolution
        a	:     int						      =    do_resize
        a	:     List[str]						      =    size
        a	:     List[str]						      =    size_divisor
        a	:     Union[str, Any]						      =    do_normalize
        a	:     str						      =    image_mean
        a	:     List[Any]						      =    image_std
   def _lowercase(     self     )						->int:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=False     )						->Optional[int]:
        if not batched:
             a	:     List[str]						      =    self.size['''shortest_edge''']
             a	:     List[str]						      =    image_inputs[0]
             if isinstance(lowerCAmelCase_				,  Image.Image     ):
                  a      ,				a	:     Union[str, Any]						      =    image.size
             else:
                  a      ,				a	:     str						      =    image.shape[1], image.shape[2]
             a	:     str						      =    size / min(lowerCAmelCase_				,  lowerCAmelCase_     )
             if h < w:
                  a      ,				a	:     Optional[int]						      =    size, scale * w
             else:
                  a      ,				a	:     Optional[Any]						      =    scale * h, size

             a	:     Dict						      =    int((13_33 / 8_00) * size     )
             if max(lowerCAmelCase_				,  lowerCAmelCase_     ) > max_size:
                  a	:     List[str]						      =    max_size / max(lowerCAmelCase_				,  lowerCAmelCase_     )
                  a	:     Optional[Any]						      =    newh * scale
                  a	:     Tuple						      =    neww * scale

             a      ,				a	:     str						      =    int(newh + 0.5     ), int(neww + 0.5     )
             a      ,				a	:     List[Any]						      =    (
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

        else:
             a	:     Dict						      =    []
             for image in image_inputs:
                  a      ,				a	:     Union[str, Any]						      =    self.get_expected_values([image]     )
                  expected_values.append((expected_height, expected_width)     )
             a	:     List[str]						      =    max(lowerCAmelCase_				,  key=lambda lowerCAmelCase_     : item[0]     )[0]
             a	:     Optional[int]						      =    max(lowerCAmelCase_				,  key=lambda lowerCAmelCase_     : item[1]     )[1]

        return expected_height, expected_width






@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE( snake_case__							,   unittest.TestCase						):
   UpperCamelCase_					:    List[Any]			      = ViltImageProcessor if is_vision_available() else None
   def _lowercase(     self     )						->Any:
        a	:     Optional[Any]						      =    ViltImageProcessingTester(self     )
   @property
   def _lowercase(     self     )						->List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def _lowercase(     self     )						->int:
        a	:     int						      =    self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''image_mean'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''image_std'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''do_normalize'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''do_resize'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''size'''     )     )
        self.assertTrue(hasattr(lowerCAmelCase_				,  '''size_divisor'''     )     )
   def _lowercase(     self     )						->int:
        a	:     Optional[Any]						      =    self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size				,  {'''shortest_edge''': 30}     )

        a	:     Any						      =    self.image_processing_class.from_dict(self.image_processor_dict				,  size=42     )
        self.assertEqual(image_processor.size				,  {'''shortest_edge''': 42}     )
   def _lowercase(     self     )						->List[str]:
        pass
   def _lowercase(     self     )						->Tuple:
        # Initialize image_processing
        a	:     Dict						      =    self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        a	:     Optional[Any]						      =    prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase_     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_				,  Image.Image     )

        # Test not batched input
        a	:     Union[str, Any]						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     Any						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,  )

        # Test batched
        a	:     List[Any]						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     str						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_				,  batched=lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,  )
   def _lowercase(     self     )						->Union[str, Any]:
        # Initialize image_processing
        a	:     Dict						      =    self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        a	:     Optional[int]						      =    prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase_				,  numpify=lowerCAmelCase_     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_				,  np.ndarray     )

        # Test not batched input
        a	:     str						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     Tuple						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,  )

        # Test batched
        a	:     List[str]						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     Dict						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_				,  batched=lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,  )


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

        # Test not batched input
        a	:     Any						      =    image_processing(image_inputs[0]				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     Any						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,  )

        # Test batched
        a	:     List[str]						      =    image_processing(lowerCAmelCase_				,  return_tensors='''pt'''     ).pixel_values

        a      ,				a	:     List[str]						      =    self.image_processor_tester.get_expected_values(lowerCAmelCase_				,  batched=lowerCAmelCase_     )
        self.assertEqual(
            encoded_images.shape				,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )				,  )
import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   UpperCamelCase_					:    int			      = EsmTokenizer
   def _lowercase(     self     )						->Dict:
        super().setUp()
        a	:     Tuple						      =    tempfile.mkdtemp()
        # fmt: off
        a	:     List[str]						      =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
        # fmt: on
        a	:     Tuple						      =    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 _lowercase(     self				,  **lowerCAmelCase_     )						->List[PreTrainedTokenizerBase]:
        return [self.get_tokenizer(**lowerCAmelCase_     )]
   def _lowercase(     self				,  **lowerCAmelCase_     )						->PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname				,  **lowerCAmelCase_     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     List[Any]						      =    tokenizer.tokenize('''LAGVS'''     )
        self.assertListEqual(lowerCAmelCase_				,  ['''L''', '''A''', '''G''', '''V''', '''S''']     )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )				,  [4, 5, 6, 7, 8]     )
   def _lowercase(     self     )						->Optional[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[Any]						      =    '''LAGVS'''
        self.assertListEqual(tokenizer.encode(lowerCAmelCase_     )				,  [0, 4, 5, 6, 7, 8, 2]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Any						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[int]						      =    ['''LAGVS''', '''WCB''']
        a	:     Optional[Any]						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[Any]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     str						      =    ['''LAGVS''', '''WCB''']
        a	:     Dict						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]     )


   def _lowercase(     self     )						->List[str]:
        a	:     int						      =    self.get_tokenizers(fast=lowerCAmelCase_     )
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
                  a	:     Union[str, Any]						      =    '''<unk>'''
                  a	:     List[Any]						      =    '''<mask>'''

                  a	:     List[Any]						      =    tokenizer.tokenize(lowerCAmelCase_     )
                  a	:     Tuple						      =    tokenizer.tokenize(lowerCAmelCase_     )

                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )