"""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 LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     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      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     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
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					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      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[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
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					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      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		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
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					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 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  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			ViTImageProcessor if is_vision_available() else None






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		str		   =     (3, 32, 128)
													__lowerCamelCase					:		Optional[int]		   =     tempfile.mkdtemp()

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

													__lowerCamelCase					:		List[Any]		   =     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(UpperCAmelCase    ) + "\n"    )

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






						def       lowerCamelCase__     (       self      :			Tuple							,					**UpperCAmelCase      :			Optional[int]    ):
													return MgpstrTokenizer.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					**UpperCAmelCase      :			List[str]    ):
													return ViTImageProcessor.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													shutil.rmtree(self.tmpdirname    )






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta    )

													__lowerCamelCase					:		Union[str, Any]		   =     Image.fromarray(np.moveaxis(UpperCAmelCase							,					0							,					-1    )    )

													return image_input






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_tokenizer()
													__lowerCamelCase					:		Any		   =     self.get_image_processor()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )
													__lowerCamelCase					:		str		   =     MgpstrProcessor.from_pretrained(self.tmpdirname							,					use_fast=UpperCAmelCase    )

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

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






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()
													__lowerCamelCase					:		Dict		   =     self.get_image_processor()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )

													__lowerCamelCase					:		Dict		   =     self.get_tokenizer(bos_token="(BOS)"							,					eos_token="(EOS)"    )
													__lowerCamelCase					:		Any		   =     self.get_image_processor(do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													__lowerCamelCase					:		List[str]		   =     MgpstrProcessor.from_pretrained(
													    self.tmpdirname							,					bos_token="(BOS)"							,					eos_token="(EOS)"							,					do_normalize=UpperCAmelCase							,					padding_value=1.0    )

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

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






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.get_image_processor()
													__lowerCamelCase					:		Dict		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[int]		   =     image_processor(UpperCAmelCase							,					return_tensors="np"    )
													__lowerCamelCase					:		Tuple		   =     processor(images=UpperCAmelCase							,					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       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		int		   =     self.get_image_processor()
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()

													__lowerCamelCase					:		int		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     "test"

													__lowerCamelCase					:		List[Any]		   =     processor(text=UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     "test"
													__lowerCamelCase					:		Union[str, Any]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[Any]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

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

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






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Any		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Dict		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

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

													__lowerCamelCase					:		List[Any]		   =     processor.char_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.batch_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     [seq.replace(" "							,					""    ) for seq in decoded_tok]

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		str		   =     self.get_image_processor()
													__lowerCamelCase					:		str		   =     self.get_tokenizer()

													__lowerCamelCase					:		Union[str, Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     None
													__lowerCamelCase					:		int		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		List[str]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

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







						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Tuple		   =     self.get_tokenizer()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     torch.randn(1							,					27							,					38    )
													__lowerCamelCase					:		List[str]		   =     torch.randn(1							,					27							,					50257    )
													__lowerCamelCase					:		Union[str, Any]		   =     torch.randn(1							,					27							,					30522    )

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

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