




import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =CLIPTokenizer
						lowerCAmelCase__			      =CLIPTokenizerFast
						lowerCAmelCase__			      =True
						lowerCAmelCase__			      ={}
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								super().setUp()

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
								# fmt: on
								SCREAMING_SNAKE_CASE_					      =							dict(zip(snake_case__					,							range(len(snake_case__  )  )  )  )
								SCREAMING_SNAKE_CASE_					      =							['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
								SCREAMING_SNAKE_CASE_					      =							{'unk_token': '<unk>'}

								SCREAMING_SNAKE_CASE_					      =							os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']  )
								SCREAMING_SNAKE_CASE_					      =							os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['merges_file']  )
								with open(self.vocab_file					,							'w'					,							encoding='utf-8'  ) as fp:
										fp.write(json.dumps(snake_case__  ) + '\n'  )
								with open(self.merges_file					,							'w'					,							encoding='utf-8'  ) as fp:
										fp.write('\n'.join(snake_case__  )  )



						def 	__a					(			self	:   Dict					,							**snake_case__	:   Tuple  ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map  )
								return CLIPTokenizer.from_pretrained(self.tmpdirname					,							**snake_case__  )



						def 	__a					(			self	:   Any					,							**snake_case__	:   Dict  ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map  )
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,							**snake_case__  )



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								return input_text, output_text



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							CLIPTokenizer(self.vocab_file					,							self.merges_file					,							**self.special_tokens_map  )
								SCREAMING_SNAKE_CASE_					      =							'lower newer'
								SCREAMING_SNAKE_CASE_					      =							['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
								SCREAMING_SNAKE_CASE_					      =							tokenizer.tokenize(snake_case__  )
								self.assertListEqual(snake_case__					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokens + [tokenizer.unk_token]
								SCREAMING_SNAKE_CASE_					      =							[10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__  )					,							snake_case__  )



						@require_ftfy
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												SCREAMING_SNAKE_CASE_					      =							self.tokenizer_class.from_pretrained(snake_case__					,							**snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							self.rust_tokenizer_class.from_pretrained(snake_case__					,							**snake_case__  )

												SCREAMING_SNAKE_CASE_					      =							'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												SCREAMING_SNAKE_CASE_					      =							tokenizer_s.tokenize(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer_r.tokenize(snake_case__  )

												self.assertListEqual(snake_case__					,							snake_case__  )

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												SCREAMING_SNAKE_CASE_					      =							'xa\u0303y' + ' ' + 'x\xe3y'
												SCREAMING_SNAKE_CASE_					      =							tokenizer_s.tokenize(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							tokenizer_r.tokenize(snake_case__  )

												self.assertListEqual(snake_case__					,							snake_case__  )

												# Test that the tokenization is identical on unicode of space type
												SCREAMING_SNAKE_CASE_					      =							[
												    '\u0009',  # (horizontal tab, '\t')
												    '\u000B',  # (vertical tab)
												    '\u000C',  # (form feed)
												    '\u0020',  # (space, ' ')
												    '\u200E',  # (left-to-right mark):w
												    '\u200F',  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
														SCREAMING_SNAKE_CASE_					      =							tokenizer_s.tokenize(snake_case__  )
														SCREAMING_SNAKE_CASE_					      =							tokenizer_r.tokenize(snake_case__  )

														self.assertListEqual(snake_case__					,							snake_case__  )

												# Test that the tokenization is identical on unicode of line break type
												SCREAMING_SNAKE_CASE_					      =							[
												    '\u000A',  # (line feed, '\n')
												    '\r\n',  # (carriage return and line feed, '\r\n')
												    '\u000D',  # (carriage return, '\r')
												    '\r',  # (carriage return, '\r')
												    '\u000D',  # (carriage return, '\r')
												    '\u2028',  # (line separator)
												    '\u2029',  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
														SCREAMING_SNAKE_CASE_					      =							tokenizer_s.tokenize(snake_case__  )
														SCREAMING_SNAKE_CASE_					      =							tokenizer_r.tokenize(snake_case__  )

														self.assertListEqual(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												SCREAMING_SNAKE_CASE_					      =							'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
												SCREAMING_SNAKE_CASE_					      =							f'''{text_of_1_token} {text_of_1_token}'''

												SCREAMING_SNAKE_CASE_					      =							self.rust_tokenizer_class.from_pretrained(
												    snake_case__					,							use_fast=snake_case__					,							)
												SCREAMING_SNAKE_CASE_					      =							tokenizer_r(snake_case__					,							return_offsets_mapping=snake_case__					,							add_special_tokens=snake_case__  )
												self.assertEqual(encoding.offset_mapping[0]					,							(0, len(snake_case__  ))  )
												self.assertEqual(
												    encoding.offset_mapping[1]					,							(len(snake_case__  ) + 1, len(snake_case__  ) + 1 + len(snake_case__  ))					,							)

												SCREAMING_SNAKE_CASE_					      =							f''' {text}'''

												SCREAMING_SNAKE_CASE_					      =							self.rust_tokenizer_class.from_pretrained(
												    snake_case__					,							use_fast=snake_case__					,							)
												SCREAMING_SNAKE_CASE_					      =							tokenizer_r(snake_case__					,							return_offsets_mapping=snake_case__					,							add_special_tokens=snake_case__  )
												self.assertEqual(encoding.offset_mapping[0]					,							(1, 1 + len(snake_case__  ))  )
												self.assertEqual(
												    encoding.offset_mapping[1]					,							(1 + len(snake_case__  ) + 1, 1 + len(snake_case__  ) + 1 + len(snake_case__  ))					,							)



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								with self.assertRaises(snake_case__  ) as context:
										self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'  )

								self.assertTrue(
								    context.exception.args[0].startswith(
								        'The `backend_tokenizer` provided does not match the expected format.'  )  )



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

								"""simple docstring"""

								super().test_tokenization_python_rust_equals()






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								pass





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}""")