
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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ViTImageProcessor if is_vision_available() else None

			@property
			def       lowerCAmelCase__	(       self :       str      ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						(3, 32, 128)
							UpperCamelCase_:							Optional[Any]	=						tempfile.mkdtemp()

							# fmt: off
							UpperCamelCase_:							Any	=						["""[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_:							Optional[Any]	=						dict(zip(snake_case_						,	range(len(snake_case_      )      )      )      )

							UpperCamelCase_:							str	=						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"""      )

							UpperCamelCase_:							List[Any]	=						{
							    """do_normalize""": False,
							    """do_resize""": True,
							    """image_processor_type""": """ViTImageProcessor""",
							    """resample""": 3,
							    """size""": {"""height""": 32, """width""": 128},
							}
							UpperCamelCase_:							Any	=						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 :       List[Any]						,	**snake_case_ :       List[Any]      ):
							return MgpstrTokenizer.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	**snake_case_ :       str      ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							int	=						np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta      )

							UpperCamelCase_:							Any	=						Image.fromarray(np.moveaxis(snake_case_						,	0						,	-1      )      )

							return image_input

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_tokenizer()
							UpperCamelCase_:							Any	=						self.get_image_processor()

							UpperCamelCase_:							Any	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Optional[int]	=						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 :       Optional[int]      ):
							UpperCamelCase_:							str	=						self.get_tokenizer()
							UpperCamelCase_:							List[str]	=						self.get_image_processor()

							UpperCamelCase_:							List[str]	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )
							processor.save_pretrained(self.tmpdirname      )

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

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

							UpperCamelCase_:							Dict	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

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

							UpperCamelCase_:							Optional[int]	=						image_processor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							Union[str, Any]	=						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 :       Any      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()

							UpperCamelCase_:							str	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""test"""

							UpperCamelCase_:							Dict	=						processor(text=snake_case_      )

							UpperCamelCase_:							Any	=						tokenizer(snake_case_      )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_image_processor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							int	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Tuple	=						"""test"""
							UpperCamelCase_:							int	=						self.prepare_image_inputs()

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

							UpperCamelCase_:							Any	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

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

							UpperCamelCase_:							Union[str, Any]	=						processor.char_decode(snake_case_      )
							UpperCamelCase_:							int	=						tokenizer.batch_decode(snake_case_      )
							UpperCamelCase_:							int	=						[seq.replace(""" """						,	""""""      ) for seq in decoded_tok]

							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Dict	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						None
							UpperCamelCase_:							Optional[int]	=						self.prepare_image_inputs()

							UpperCamelCase_:							List[str]	=						processor(text=snake_case_						,	images=snake_case_      )

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




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							str	=						self.get_image_processor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Union[str, Any]	=						MgpstrProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Any	=						torch.randn(1						,	27						,	38      )
							UpperCamelCase_:							int	=						torch.randn(1						,	27						,	5_0257      )
							UpperCamelCase_:							Optional[int]	=						torch.randn(1						,	27						,	3_0522      )

							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"""]      )








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






