




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    lowercase_		(unittest.TestCase ):
						lowerCAmelCase__			      =ViTImageProcessor if is_vision_available() else None



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.image_processor_tester.prepare_image_processor_dict()



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							(3, 32, 1_28)
								SCREAMING_SNAKE_CASE_					      =							tempfile.mkdtemp()

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							['[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
								SCREAMING_SNAKE_CASE_					      =							dict(zip(snake_case__					,							range(len(snake_case__  )  )  )  )

								SCREAMING_SNAKE_CASE_					      =							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'  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 1_28},
								}
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   List[str]					,							**snake_case__	:   Any  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]					,							**snake_case__	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								shutil.rmtree(self.tmpdirname  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							Image.fromarray(np.moveaxis(snake_case__					,							0					,							-1  )  )

								return image_input



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()
								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )
								processor.save_pretrained(self.tmpdirname  )
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()
								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )
								processor.save_pretrained(self.tmpdirname  )

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer(bos_token='(BOS)'					,							eos_token='(EOS)'  )
								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor(do_normalize=snake_case__					,							padding_value=1.0  )

								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_					      =							image_processor(snake_case__					,							return_tensors='np'  )
								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'test'

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__  )

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



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'test'
								SCREAMING_SNAKE_CASE_					      =							self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							processor.char_decode(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.batch_decode(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[seq.replace(' '					,							''  ) for seq in decoded_tok]

								self.assertListEqual(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							None
								SCREAMING_SNAKE_CASE_					      =							self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__					,							images=snake_case__  )

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






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							MgpstrProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							torch.randn(1					,							27					,							38  )
								SCREAMING_SNAKE_CASE_					      =							torch.randn(1					,							27					,							5_02_57  )
								SCREAMING_SNAKE_CASE_					      =							torch.randn(1					,							27					,							3_05_22  )

								SCREAMING_SNAKE_CASE_					      =							processor.batch_decode([char_input, bpe_input, wp_input]  )

								self.assertListEqual(list(results.keys()  )					,							['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']  )





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")