


"""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"""






		_lowerCamelCase			  =       ViTImageProcessor if is_vision_available() else None





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()





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







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			(3, 3_2, 1_2_8)
								snake_case_   : str						=			tempfile.mkdtemp()

								# fmt: off
								snake_case_   : 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
								snake_case_   : str						=			dict(zip(_UpperCamelCase							,    range(len(_UpperCamelCase   )   )   )   )

								snake_case_   : 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(_UpperCamelCase   ) + """\n"""   )

								snake_case_   : Any						=			{
								    """do_normalize""": False,
								    """do_resize""": True,
								    """image_processor_type""": """ViTImageProcessor""",
								    """resample""": 3,
								    """size""": {"""height""": 3_2, """width""": 1_2_8},
								}
								snake_case_   : Union[str, Any]						=			os.path.join(self.tmpdirname							,    _UpperCamelCase   )
								with open(self.image_processor_file							,    """w"""							,    encoding="""utf-8"""   ) as fp:
														json.dump(_UpperCamelCase							,    _UpperCamelCase   )





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Tuple:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						List[Any]:







								'''simple docstring'''

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





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







								'''simple docstring'''

								shutil.rmtree(self.tmpdirname   )





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Tuple						=			np.random.randint(2_5_5							,    size=(3, 3_0, 4_0_0)							,    dtype=np.uinta   )

								snake_case_   : int						=			Image.fromarray(np.moveaxis(_UpperCamelCase							,    0							,    -1   )   )

								return image_input





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

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

								snake_case_   : Union[str, Any]						=			MgpstrProcessor(tokenizer=_UpperCamelCase							,    image_processor=_UpperCamelCase   )
								processor.save_pretrained(self.tmpdirname   )
								snake_case_   : int						=			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    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

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

								snake_case_   : Any						=			MgpstrProcessor(tokenizer=_UpperCamelCase							,    image_processor=_UpperCamelCase   )
								processor.save_pretrained(self.tmpdirname   )

								snake_case_   : Tuple						=			self.get_tokenizer(bos_token="""(BOS)"""							,    eos_token="""(EOS)"""   )
								snake_case_   : Union[str, Any]						=			self.get_image_processor(do_normalize=_UpperCamelCase							,    padding_value=1.0   )

								snake_case_   : Tuple						=			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    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

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

								snake_case_   : List[str]						=			MgpstrProcessor(tokenizer=_UpperCamelCase							,    image_processor=_UpperCamelCase   )

								snake_case_   : List[str]						=			self.prepare_image_inputs()

								snake_case_   : Optional[int]						=			image_processor(_UpperCamelCase							,    return_tensors="""np"""   )
								snake_case_   : int						=			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    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

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

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

								snake_case_   : Optional[int]						=			"""test"""

								snake_case_   : Union[str, Any]						=			processor(text=_UpperCamelCase   )

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





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







								'''simple docstring'''

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

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

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

								snake_case_   : int						=			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    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

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

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

								snake_case_   : int						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

								snake_case_   : Any						=			processor.char_decode(_UpperCamelCase   )
								snake_case_   : Tuple						=			tokenizer.batch_decode(_UpperCamelCase   )
								snake_case_   : List[str]						=			[seq.replace(""" """							,    """"""   ) for seq in decoded_tok]

								self.assertListEqual(_UpperCamelCase							,    _UpperCamelCase   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Dict						=			self.get_image_processor()
								snake_case_   : str						=			self.get_tokenizer()

								snake_case_   : List[Any]						=			MgpstrProcessor(tokenizer=_UpperCamelCase							,    image_processor=_UpperCamelCase   )

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

								snake_case_   : Union[str, Any]						=			processor(text=_UpperCamelCase							,    images=_UpperCamelCase   )

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





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







								'''simple docstring'''

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

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

								snake_case_   : Any						=			torch.randn(1							,    2_7							,    3_8   )
								snake_case_   : List[Any]						=			torch.randn(1							,    2_7							,    5_0_2_5_7   )
								snake_case_   : List[str]						=			torch.randn(1							,    2_7							,    3_0_5_2_2   )

								snake_case_   : 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"""





from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def is_in_circle(__UpperCamelCase      :			float					, __UpperCamelCase      :			float    ) -> bool:
												snake_case_   : Dict						=			sqrt((x**2) + (y**2)    )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						snake_case_   : Tuple						=			mean(
						    int(is_in_circle(uniform(-1.0					, 1.0    )					, uniform(-1.0					, 1.0    )    )    )
						    for _ in range(__UpperCamelCase    )    )
						# The ratio of the area for circle to square is pi/4.
						snake_case_   : Union[str, Any]						=			proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'    )
						print(F'The numpy value of pi is {pi}'    )
						print(F'The total error is {abs(pi - pi_estimate    )}'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Callable[[float], float]					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0					, ):


						'''simple docstring'''






						return mean(
						    function_to_integrate(uniform(__UpperCamelCase					, __UpperCamelCase    )    ) for _ in range(__UpperCamelCase    )    ) * (max_value - min_value)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0    ):


						'''simple docstring'''






						def identity_function(__UpperCamelCase      :			float    ) -> float:
												return x

						snake_case_   : int						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : str						=			(max_value * max_value - min_value * min_value) / 2

						print("""******************"""    )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {expected_value}'    )
						print(F'Total error is {abs(estimated_value - expected_value    )}'    )
						print("""******************"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def function_to_integrate(__UpperCamelCase      :			float    ) -> float:
												return sqrt(4.0 - x * x    )

						snake_case_   : List[Any]						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, 0.0					, 2.0    )

						print("""******************"""    )
						print("""Estimating pi using area_under_curve_estimator"""    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {pi}'    )
						print(F'Total error is {abs(estimated_value - pi    )}'    )
						print("""******************"""    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

