


'''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		):
			lowerCAmelCase       :		Optional[Any]				      =   ViTImageProcessor if is_vision_available() else None




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



					'''simple docstring'''







					return self.image_processor_tester.prepare_image_processor_dict()




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					(3, 32, 1_28)
					_UpperCAmelCase						:				Tuple	    =					tempfile.mkdtemp()

					# fmt: off
					_UpperCAmelCase						:				Dict	    =					["[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						:				Union[str, Any]	    =					dict(zip(lowerCamelCase__		,     range(len(lowerCamelCase__       )       )       )       )

					_UpperCAmelCase						:				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(lowerCamelCase__       ) + "\n"       )

					_UpperCAmelCase						:				Union[str, Any]	    =					{
					    "do_normalize": False,
					    "do_resize": True,
					    "image_processor_type": "ViTImageProcessor",
					    "resample": 3,
					    "size": {"height": 32, "width": 1_28},
					}
					_UpperCAmelCase						:				Optional[int]	    =					os.path.join(self.tmpdirname		,     lowerCamelCase__       )
					with open(self.image_processor_file		,     "w"		,     encoding="utf-8"       ) as fp:
							json.dump(lowerCamelCase__		,     lowerCamelCase__       )




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



					'''simple docstring'''







					return MgpstrTokenizer.from_pretrained(self.tmpdirname		,     **lowerCamelCase__       )




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



					'''simple docstring'''







					return ViTImageProcessor.from_pretrained(self.tmpdirname		,     **lowerCamelCase__       )




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



					'''simple docstring'''







					shutil.rmtree(self.tmpdirname       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					np.random.randint(2_55		,     size=(3, 30, 4_00)		,     dtype=np.uinta       )

					_UpperCAmelCase						:				Tuple	    =					Image.fromarray(np.moveaxis(lowerCamelCase__		,     0		,     -1       )       )

					return image_input




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					self.get_tokenizer()
					_UpperCAmelCase						:				int	    =					self.get_image_processor()

					_UpperCAmelCase						:				Optional[int]	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )
					processor.save_pretrained(self.tmpdirname       )
					_UpperCAmelCase						:				Optional[int]	    =					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 							lowerCAmelCase__			(						self     :   str       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.get_tokenizer()
					_UpperCAmelCase						:				List[Any]	    =					self.get_image_processor()

					_UpperCAmelCase						:				Union[str, Any]	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )
					processor.save_pretrained(self.tmpdirname       )

					_UpperCAmelCase						:				List[str]	    =					self.get_tokenizer(bos_token="(BOS)"		,     eos_token="(EOS)"       )
					_UpperCAmelCase						:				Optional[Any]	    =					self.get_image_processor(do_normalize=lowerCamelCase__		,     padding_value=1.0       )

					_UpperCAmelCase						:				List[str]	    =					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 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.get_image_processor()
					_UpperCAmelCase						:				str	    =					self.get_tokenizer()

					_UpperCAmelCase						:				List[str]	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

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

					_UpperCAmelCase						:				List[str]	    =					image_processor(lowerCamelCase__		,     return_tensors="np"       )
					_UpperCAmelCase						:				Optional[int]	    =					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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_image_processor()
					_UpperCAmelCase						:				Tuple	    =					self.get_tokenizer()

					_UpperCAmelCase						:				Any	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					"test"

					_UpperCAmelCase						:				int	    =					processor(text=lowerCamelCase__       )

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




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_image_processor()
					_UpperCAmelCase						:				List[Any]	    =					self.get_tokenizer()

					_UpperCAmelCase						:				int	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

					_UpperCAmelCase						:				List[str]	    =					"test"
					_UpperCAmelCase						:				Optional[Any]	    =					self.prepare_image_inputs()

					_UpperCAmelCase						:				Any	    =					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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.get_image_processor()
					_UpperCAmelCase						:				str	    =					self.get_tokenizer()

					_UpperCAmelCase						:				List[Any]	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

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

					_UpperCAmelCase						:				Optional[int]	    =					processor.char_decode(lowerCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					tokenizer.batch_decode(lowerCamelCase__       )
					_UpperCAmelCase						:				int	    =					[seq.replace(" "		,     ""       ) for seq in decoded_tok]

					self.assertListEqual(lowerCamelCase__		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_image_processor()
					_UpperCAmelCase						:				str	    =					self.get_tokenizer()

					_UpperCAmelCase						:				str	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

					_UpperCAmelCase						:				str	    =					None
					_UpperCAmelCase						:				List[Any]	    =					self.prepare_image_inputs()

					_UpperCAmelCase						:				Dict	    =					processor(text=lowerCamelCase__		,     images=lowerCamelCase__       )

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






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



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					self.get_image_processor()
					_UpperCAmelCase						:				int	    =					self.get_tokenizer()

					_UpperCAmelCase						:				str	    =					MgpstrProcessor(tokenizer=lowerCamelCase__		,     image_processor=lowerCamelCase__       )

					_UpperCAmelCase						:				Tuple	    =					torch.randn(1		,     27		,     38       )
					_UpperCAmelCase						:				Any	    =					torch.randn(1		,     27		,     5_02_57       )
					_UpperCAmelCase						:				List[str]	    =					torch.randn(1		,     27		,     3_05_22       )

					_UpperCAmelCase						:				List[str]	    =					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 warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



