





'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor






class UpperCAmelCase							(		unittest.TestCase							):



    def __init__(  self     ,			__A     ,			__A=7     ,			__A=3     ,			__A=18     ,			__A=30     ,			__A=400     ,			__A=True     ,			__A=None     ,			__A=True     ,			__A=[0.5, 0.5, 0.5]     ,			__A=[0.5, 0.5, 0.5]     ,			):
     __UpperCAmelCase					=   size if size is not None else {'height': 18, 'width': 18}
     __UpperCAmelCase					=   parent
     __UpperCAmelCase					=   batch_size
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   image_size
     __UpperCAmelCase					=   min_resolution
     __UpperCAmelCase					=   max_resolution
     __UpperCAmelCase					=   do_resize
     __UpperCAmelCase					=   size
     __UpperCAmelCase					=   do_normalize
     __UpperCAmelCase					=   image_mean
     __UpperCAmelCase					=   image_std




    def 					__lowerCamelCase						(  self					):
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }






@require_torch
@require_vision
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			Tuple						   =       DPTImageProcessor if is_vision_available() else None



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   DPTImageProcessingTester(self					)



    @property
    def 					__lowerCamelCase						(  self					):
     return self.image_processor_tester.prepare_image_processor_dict()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     self.assertTrue(hasattr(__A     ,			'image_mean'					)					)
     self.assertTrue(hasattr(__A     ,			'image_std'					)					)
     self.assertTrue(hasattr(__A     ,			'do_normalize'					)					)
     self.assertTrue(hasattr(__A     ,			'do_resize'					)					)
     self.assertTrue(hasattr(__A     ,			'size'					)					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.image_processing_class.from_dict(self.image_processor_dict					)
     self.assertEqual(image_processor.size     ,			{'height': 18, 'width': 18}					)

     __UpperCAmelCase					=   self.image_processing_class.from_dict(self.image_processor_dict     ,			size=42					)
     self.assertEqual(image_processor.size     ,			{'height': 42, 'width': 42}					)



    def 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random PIL images
     __UpperCAmelCase					=   prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			Image.Image					)

     # Test not batched input
     __UpperCAmelCase					=   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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)

     # Test batched
     __UpperCAmelCase					=   image_processing(__A     ,			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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)



    def 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random numpy tensors
     __UpperCAmelCase					=   prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__A     ,			numpify=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			np.ndarray					)

     # Test not batched input
     __UpperCAmelCase					=   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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)

     # Test batched
     __UpperCAmelCase					=   image_processing(__A     ,			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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)




    def 					__lowerCamelCase						(  self					):
     # Initialize image_processing
     __UpperCAmelCase					=   self.image_processing_class(**self.image_processor_dict					)
     # create random PyTorch tensors
     __UpperCAmelCase					=   prepare_image_inputs(self.image_processor_tester     ,			equal_resolution=__A     ,			torchify=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			torch.Tensor					)

     # Test not batched input
     __UpperCAmelCase					=   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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)

     # Test batched
     __UpperCAmelCase					=   image_processing(__A     ,			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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,			)







'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       MgpstrTokenizer
    _A :			Any						   =       False
    _A :			List[Any]						   =       {}
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
     with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      fp.write(json.dumps(__A					) + '\n'					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'tester'
     __UpperCAmelCase					=   'tester'
     return input_text, output_text



    @unittest.skip('MGP-STR always lower cases letters.'					)
    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers(do_lower_case=__A					)
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					=   '[SPECIAL_TOKEN]'

       tokenizer.add_special_tokens({'cls_token': special_token}					)
       __UpperCAmelCase					=   tokenizer.encode([special_token]     ,			add_special_tokens=__A					)
       self.assertEqual(len(__A					)     ,			1					)

       __UpperCAmelCase					=   tokenizer.decode(__A     ,			skip_special_tokens=__A					)
       self.assertTrue(special_token not in decoded					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_input_output_texts(__A					)

       __UpperCAmelCase					=   tokenizer.tokenize(__A					)
       __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
       __UpperCAmelCase					=   tokenizer.encode(__A     ,			add_special_tokens=__A					)
       self.assertListEqual(__A     ,			__A					)

       __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
       self.assertNotEqual(len(__A					)     ,			0					)
       __UpperCAmelCase					=   tokenizer.decode(__A					)
       self.assertIsInstance(__A     ,			__A					)

       self.assertEqual(text_a.replace(' '     ,			''					)     ,			__A					)



    @unittest.skip('MGP-STR tokenizer only handles one sequence.'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'					)
    def 					__lowerCamelCase						(  self					):
     pass





