




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
					import torch


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor, ViTImageProcessor







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

			"""simple docstring"""







			snake_case_   		=       ViTImageProcessor if is_vision_available() else None


			@property
			def       lowercase_    (      self							)    ->      Optional[Any]:



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


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



							'''simple docstring'''
							__lowerCamelCase		       =    (3, 32, 128)
							__lowerCamelCase		       =    tempfile.mkdtemp()

							# fmt: off
							__lowerCamelCase		       =    ["[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
							__lowerCamelCase		       =    dict(zip(lowerCamelCase__			,						range(len(lowerCamelCase__							)							)							)							)

							__lowerCamelCase		       =    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(lowerCamelCase__							) + '\n'							)

							__lowerCamelCase		       =    {
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 128},
							}
							__lowerCamelCase		       =    os.path.join(self.tmpdirname			,						lowerCamelCase__							)
							with open(self.image_processor_file			,						'w'			,						encoding='utf-8'							) as fp:
											json.dump(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							return MgpstrTokenizer.from_pretrained(self.tmpdirname			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							return ViTImageProcessor.from_pretrained(self.tmpdirname			,						**lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							shutil.rmtree(self.tmpdirname							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randint(255			,						size=(3, 30, 400)			,						dtype=np.uinta							)

							__lowerCamelCase		       =    Image.fromarray(np.moveaxis(lowerCamelCase__			,						0			,						-1							)							)

							return image_input


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_tokenizer()
							__lowerCamelCase		       =    self.get_image_processor()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)
							processor.save_pretrained(self.tmpdirname							)
							__lowerCamelCase		       =    MgpstrProcessor.from_pretrained(self.tmpdirname			,						use_fast=lowerCamelCase__							)

							self.assertEqual(processor.char_tokenizer.get_vocab()			,						tokenizer.get_vocab()							)
							self.assertIsInstance(processor.char_tokenizer			,						lowerCamelCase__							)

							self.assertEqual(processor.image_processor.to_json_string()			,						image_processor.to_json_string()							)
							self.assertIsInstance(processor.image_processor			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_tokenizer()
							__lowerCamelCase		       =    self.get_image_processor()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)
							processor.save_pretrained(self.tmpdirname							)

							__lowerCamelCase		       =    self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'							)
							__lowerCamelCase		       =    self.get_image_processor(do_normalize=lowerCamelCase__			,						padding_value=1.0							)

							__lowerCamelCase		       =    MgpstrProcessor.from_pretrained(
							    self.tmpdirname			,						bos_token='(BOS)'			,						eos_token='(EOS)'			,						do_normalize=lowerCamelCase__			,						padding_value=1.0							)

							self.assertEqual(processor.char_tokenizer.get_vocab()			,						tokenizer_add_kwargs.get_vocab()							)
							self.assertIsInstance(processor.char_tokenizer			,						lowerCamelCase__							)

							self.assertEqual(processor.image_processor.to_json_string()			,						image_processor_add_kwargs.to_json_string()							)
							self.assertIsInstance(processor.image_processor			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    self.prepare_image_inputs()

							__lowerCamelCase		       =    image_processor(lowerCamelCase__			,						return_tensors='np'							)
							__lowerCamelCase		       =    processor(images=lowerCamelCase__			,						return_tensors='np'							)

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()			,						input_processor[key].sum()			,						delta=1e-2							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    "test"

							__lowerCamelCase		       =    processor(text=lowerCamelCase__							)

							__lowerCamelCase		       =    tokenizer(lowerCamelCase__							)
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]			,						encoded_processor[key]							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    "test"
							__lowerCamelCase		       =    self.prepare_image_inputs()

							__lowerCamelCase		       =    processor(text=lowerCamelCase__			,						images=lowerCamelCase__							)

							self.assertListEqual(list(inputs.keys()							)			,						['pixel_values', 'labels']							)

							# test if it raises when no input is passed
							with pytest.raises(lowerCamelCase__							):
											processor()


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							__lowerCamelCase		       =    processor.char_decode(lowerCamelCase__							)
							__lowerCamelCase		       =    tokenizer.batch_decode(lowerCamelCase__							)
							__lowerCamelCase		       =    [seq.replace(' '			,						''							) for seq in decoded_tok]

							self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    self.prepare_image_inputs()

							__lowerCamelCase		       =    processor(text=lowerCamelCase__			,						images=lowerCamelCase__							)

							self.assertListEqual(list(inputs.keys()							)			,						processor.model_input_names							)






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_image_processor()
							__lowerCamelCase		       =    self.get_tokenizer()

							__lowerCamelCase		       =    MgpstrProcessor(tokenizer=lowerCamelCase__			,						image_processor=lowerCamelCase__							)

							__lowerCamelCase		       =    torch.randn(1			,						27			,						38							)
							__lowerCamelCase		       =    torch.randn(1			,						27			,						50_257							)
							__lowerCamelCase		       =    torch.randn(1			,						27			,						30_522							)

							__lowerCamelCase		       =    processor.batch_decode([char_input, bpe_input, wp_input]							)

							self.assertListEqual(list(results.keys()							)			,						['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']							)



'''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						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             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_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    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}       )

             _UpperCAmelCase     :  Dict							      =    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      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    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     :  List[Any]							      =    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
             _UpperCAmelCase     :  Optional[Any]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    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     :  Optional[int]							      =    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
             _UpperCAmelCase     :  Optional[int]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[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
             _UpperCAmelCase     :  List[str]							      =    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
             _UpperCAmelCase     :  Any							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

