import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__		:					int						=					get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     GPTSwaTokenizer
  lowercase_			=     False
  lowercase_			=     True
  lowercase_			=     False




  def 	__lowercase(      self     :	int				)->       Any:




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

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Dict			=		GPTSwaTokenizer(a_     , eos_token='<unk>'     , bos_token='<unk>'     , pad_token='<unk>'				)

       tokenizer.save_pretrained(self.tmpdirname				)




  def 	__lowercase(      self     :	Any     , a_     :	Optional[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'This is a test'
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'This is a test'
       return input_text, output_text




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		'<s>'
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_				)     , a_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_				)     , a_				)




  def 	__lowercase(      self     :	Dict				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<unk>'				)
       self.assertEqual(vocab_keys[1]     , '<s>'				)
       self.assertEqual(vocab_keys[-1]     , 'j'				)
       self.assertEqual(len(a_				)     , 2000				)




  def 	__lowercase(      self     :	Tuple				)->       Optional[int]:




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 2000				)




  def 	__lowercase(      self     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		GPTSwaTokenizer(a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tokenizer.tokenize('This is a test'				)
       self.assertListEqual(a_     , ['▁This', '▁is', '▁a', '▁t', 'est']				)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(a_				)     , [465, 287, 265, 631, 842]				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
       # fmt: off
       self.assertListEqual(
           a_     , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']     , )
       # fmt: on

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer.convert_tokens_to_ids(a_				)
       self.assertListEqual(
           a_     , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]     , )

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer.convert_ids_to_tokens(a_				)
       # fmt: off
       self.assertListEqual(
           a_     , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']				)
       # fmt: on




  def 	__lowercase(      self     :	Any				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		GPTSwaTokenizer(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		['This is a test', 'I was born in 92000, and this is falsé.']
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[
           [465, 287, 265, 631, 842],
           [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
       ]

       # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
       for text, expected_ids in zip(a_     , a_				):
            self.assertListEqual(tokenizer.encode_fast(a_				)     , a_				)

       # Test that decode_fast returns the input text
       for text, token_ids in zip(a_     , a_				):
            self.assertEqual(tokenizer.decode_fast(a_				)     , a_				)




  @slow
  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
           'Hey there, how are you doing this fine day?',
           'This is a text with a trailing spaces followed by a dot     .',
           'Häj sväjs lillebrör! =)',
           'Det är inget fel på Mr. Cool',
       ]

       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Any			=		{'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
       # fmt: on
       self.tokenizer_integration_test_util(
           expected_encoding=a_     , model_name='AI-Sweden/gpt-sw3-126m'     , sequences=a_     , )




import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).raw				).convert('RGB'				)
       return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




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




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(a_				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		image_processor(
                     image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Any			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , numpify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , np.ndarray				)

       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				str			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




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




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




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




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )



