



"""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     A__     (						unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			ViTImageProcessor if is_vision_available() else None

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> int:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

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

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= (3, 32, 128)
				__lowerCAmelCase   :						Tuple				= 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   :						Optional[Any]				= dict(zip(_SCREAMING_SNAKE_CASE ,    range(len(_SCREAMING_SNAKE_CASE))))

				__lowerCAmelCase   :						int				= 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(_SCREAMING_SNAKE_CASE) + "\n")

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

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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    **_SCREAMING_SNAKE_CASE:	int)       -> str:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname)

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

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta)

				__lowerCAmelCase   :						Any				= Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE ,    0 ,    -1))

				return image_input

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

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= self.get_tokenizer()
				__lowerCAmelCase   :						Union[str, Any]				= self.get_image_processor()

				__lowerCAmelCase   :						Optional[Any]				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)
				processor.save_pretrained(self.tmpdirname)
				__lowerCAmelCase   :						Dict				= MgpstrProcessor.from_pretrained(self.tmpdirname ,    use_fast=_SCREAMING_SNAKE_CASE)

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

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

				__lowerCAmelCase   :						Optional[Any]				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)
				processor.save_pretrained(self.tmpdirname)

				__lowerCAmelCase   :						Tuple				= self.get_tokenizer(bos_token="(BOS)" ,    eos_token="(EOS)")
				__lowerCAmelCase   :						Any				= self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE ,    padding_value=1.0)

				__lowerCAmelCase   :						Optional[Any]				= MgpstrProcessor.from_pretrained(
				    self.tmpdirname ,    bos_token="(BOS)" ,    eos_token="(EOS)" ,    do_normalize=_SCREAMING_SNAKE_CASE ,    padding_value=1.0)

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.get_image_processor()
				__lowerCAmelCase   :						Dict				= self.get_tokenizer()

				__lowerCAmelCase   :						Any				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= self.prepare_image_inputs()

				__lowerCAmelCase   :						str				= image_processor(_SCREAMING_SNAKE_CASE ,    return_tensors="np")
				__lowerCAmelCase   :						Union[str, Any]				= processor(images=_SCREAMING_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 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.get_image_processor()
				__lowerCAmelCase   :						Dict				= self.get_tokenizer()

				__lowerCAmelCase   :						Tuple				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= "test"

				__lowerCAmelCase   :						List[Any]				= processor(text=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= tokenizer(_SCREAMING_SNAKE_CASE)
				for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] ,    encoded_processor[key])

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

				"""simple docstring"""

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

				__lowerCAmelCase   :						Optional[int]				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

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

				__lowerCAmelCase   :						Optional[int]				= processor(text=_SCREAMING_SNAKE_CASE ,    images=_SCREAMING_SNAKE_CASE)

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

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.get_image_processor()
				__lowerCAmelCase   :						Union[str, Any]				= self.get_tokenizer()

				__lowerCAmelCase   :						Any				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

				__lowerCAmelCase   :						Optional[Any]				= processor.char_decode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= tokenizer.batch_decode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= [seq.replace(" " ,    "") for seq in decoded_tok]

				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

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

				__lowerCAmelCase   :						Union[str, Any]				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= None
				__lowerCAmelCase   :						Optional[int]				= self.prepare_image_inputs()

				__lowerCAmelCase   :						List[Any]				= processor(text=_SCREAMING_SNAKE_CASE ,    images=_SCREAMING_SNAKE_CASE)

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







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

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.get_image_processor()
				__lowerCAmelCase   :						Optional[int]				= self.get_tokenizer()

				__lowerCAmelCase   :						List[Any]				= MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,    image_processor=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= torch.randn(1 ,    27 ,    38)
				__lowerCAmelCase   :						Optional[int]				= torch.randn(1 ,    27 ,    5_0257)
				__lowerCAmelCase   :						int				= torch.randn(1 ,    27 ,    3_0522)

				__lowerCAmelCase   :						int				= 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"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     list:
			__lowerCAmelCase   :						List[str]				= len(__snake_case			)
			__lowerCAmelCase   :						Dict				= []
			for i in range(len(__snake_case			) - pat_len + 1			):
						__lowerCAmelCase   :						List[Any]				= True
						for j in range(__snake_case			):
									if s[i + j] != pattern[j]:
												__lowerCAmelCase   :						Union[str, Any]				= False
												break
						if match_found:
									position.append(__snake_case			)
			return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))