
"""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 __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				ViTImageProcessor if is_vision_available() else None




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







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




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







								"""simple docstring"""
								A_				 =						(3, 32, 128)
								A_				 =						tempfile.mkdtemp()

								# fmt: off
								A_				 =						["[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
								A_				 =						dict(zip(_snake_case       ,   range(len(_snake_case	)	)	)	)

								A_				 =						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(_snake_case	) + "\n"	)

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




			def      lowerCamelCase__     (				self						:					Tuple       ,   **_snake_case						:					List[str]	)      -> int:







								"""simple docstring"""
								return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   **_snake_case						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								return ViTImageProcessor.from_pretrained(self.tmpdirname       ,   **_snake_case	)




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







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




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







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

								A_				 =						Image.fromarray(np.moveaxis(_snake_case       ,   0       ,   -1	)	)

								return image_input




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







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						self.get_image_processor()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)
								processor.save_pretrained(self.tmpdirname	)
								A_				 =						MgpstrProcessor.from_pretrained(self.tmpdirname       ,   use_fast=_snake_case	)

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

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




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







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						self.get_image_processor()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)
								processor.save_pretrained(self.tmpdirname	)

								A_				 =						self.get_tokenizer(bos_token="(BOS)"       ,   eos_token="(EOS)"	)
								A_				 =						self.get_image_processor(do_normalize=_snake_case       ,   padding_value=1.0	)

								A_				 =						MgpstrProcessor.from_pretrained(
								    self.tmpdirname       ,   bos_token="(BOS)"       ,   eos_token="(EOS)"       ,   do_normalize=_snake_case       ,   padding_value=1.0	)

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

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




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







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						self.prepare_image_inputs()

								A_				 =						image_processor(_snake_case       ,   return_tensors="np"	)
								A_				 =						processor(images=_snake_case       ,   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						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						"test"

								A_				 =						processor(text=_snake_case	)

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




			def      lowerCamelCase__     (				self						:					str	)      -> int:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

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

								A_				 =						processor(text=_snake_case       ,   images=_snake_case	)

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

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




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

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

								A_				 =						processor.char_decode(_snake_case	)
								A_				 =						tokenizer.batch_decode(_snake_case	)
								A_				 =						[seq.replace(" "       ,   ""	) for seq in decoded_tok]

								self.assertListEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						None
								A_				 =						self.prepare_image_inputs()

								A_				 =						processor(text=_snake_case       ,   images=_snake_case	)

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






			def      lowerCamelCase__     (				self						:					Dict	)      -> str:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

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

								A_				 =						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 torch
def 							A_     ():

					'''simple docstring'''





					if torch.cuda.is_available():
										A_				 =						torch.cuda.device_count()
					else:
										A_				 =						0
					print(f'Successfully ran on {num_gpus} GPUs'				)


if __name__ == "__main__":
		main()




