



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	UpperCAmelCase							 =					"""pt"""
elif is_tf_available():
	UpperCAmelCase							 =					"""tf"""
else:
	UpperCAmelCase							 =					"""jax"""

class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					ByTaTokenizer
							UpperCamelCase__				:      int         =					False


							def _a      (							self		):
											super().setUp()
											a__				      =		ByTaTokenizer()
											tokenizer.save_pretrained(self.tmpdirname		)


							@cached_property
							def _a      (							self		):
											return ByTaTokenizer.from_pretrained("""google/byt5-small"""		)


							def _a      (							self  ,  **a_		):
											return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_=False  ,  a_=20  ,  a_=5		):
											# XXX The default common tokenizer tests assume that every ID is decodable on its own.
											# This assumption is invalid for ByT5 because single bytes might not be
											# valid utf-8 (byte 128 for instance).
											# Here we're overriding the smallest possible method to provide
											# a clean sequence without making the same assumption.

											a__				      =		[]
											for i in range(len(a_		)		):
															try:
																			a__				      =		tokenizer.decode([i]  ,  clean_up_tokenization_spaces=a_		)
															except UnicodeDecodeError:
																			pass
															toks.append((i, tok)		)

											a__				      =		list(filter(lambda a_		: re.match(r"""^[ a-zA-Z]+$"""  ,  t[1]		)  ,  a_		)		)
											a__				      =		list(filter(lambda a_		: [t[0]] == tokenizer.encode(t[1]  ,  add_special_tokens=a_		)  ,  a_		)		)
											if max_length is not None and len(a_		) > max_length:
															a__				      =		toks[:max_length]
											if min_length is not None and len(a_		) < min_length and len(a_		) > 0:
															while len(a_		) < min_length:
																			a__				      =		toks + toks
        # toks_str = [t[1] for t in toks]
											a__				      =		[t[0] for t in toks]

											# Ensure consistency
											a__				      =		tokenizer.decode(a_  ,  clean_up_tokenization_spaces=a_		)
											if " " not in output_txt and len(a_		) > 1:
															a__				      =		(
															    tokenizer.decode([toks_ids[0]]  ,  clean_up_tokenization_spaces=a_		)
															    + """ """
															    + tokenizer.decode(toks_ids[1:]  ,  clean_up_tokenization_spaces=a_		)
															)
											if with_prefix_space:
															a__				      =		""" """ + output_txt
											a__				      =		tokenizer.encode(a_  ,  add_special_tokens=a_		)
											return output_txt, output_ids


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]		)
											a__				      =		tokenizer(["""hi""", """I went to the gym""", """"""]		)
											self.assertListEqual(batch_with_eos_added["""input_ids"""]  ,  batch_without_eos_added["""input_ids"""]		)


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		"""Unicode €."""
											a__				      =		tokenizer(a_		)
											a__				      =		[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
											self.assertEqual(encoded["""input_ids"""]  ,  a_		)

											# decoding
											a__				      =		tokenizer.decode(a_		)
											self.assertEqual(a_  ,  """Unicode €.</s>"""		)

											a__				      =		tokenizer("""e è é ê ë"""		)
											a__				      =		[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
											self.assertEqual(encoded["""input_ids"""]  ,  a_		)
											# decoding
											a__				      =		tokenizer.decode(a_		)
											self.assertEqual(a_  ,  """e è é ê ë</s>"""		)

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		)  ,  """e è é ê ë</s>"""		)


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											# fmt: off
											a__				      =		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
											# fmt: on
											a__				      =		tokenizer(a_  ,  padding=a_  ,  return_tensors=a_		)
											self.assertIsInstance(a_  ,  a_		)

											if FRAMEWORK != "jax":
															a__				      =		list(batch.input_ids.numpy()[0]		)
											else:
															a__				      =		list(batch.input_ids.tolist()[0]		)

											self.assertListEqual(a_  ,  a_		)

											self.assertEqual((2, 37)  ,  batch.input_ids.shape		)
											self.assertEqual((2, 37)  ,  batch.attention_mask.shape		)


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											a__				      =		tokenizer(a_  ,  padding=a_  ,  return_tensors=a_		)
											# check if input_ids are returned and no decoder_input_ids
											self.assertIn("""input_ids"""  ,  a_		)
											self.assertIn("""attention_mask"""  ,  a_		)
											self.assertNotIn("""decoder_input_ids"""  ,  a_		)
											self.assertNotIn("""decoder_attention_mask"""  ,  a_		)


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		[
											    """Summary of the text.""",
											    """Another summary.""",
											]
											a__				      =		tokenizer(
											    text_target=a_  ,  max_length=32  ,  padding="""max_length"""  ,  truncation=a_  ,  return_tensors=a_		)
											self.assertEqual(32  ,  targets["""input_ids"""].shape[1]		)


							def _a      (							self		):
											a__				      =		self.ta_base_tokenizer
											a__				      =		["""A long paragraph for summarization. </s>"""]
											a__				      =		["""Summary of the text. </s>"""]
											# fmt: off
											a__				      =		[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
											a__				      =		[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
											# fmt: on

											a__				      =		tokenizer(a_  ,  text_target=a_		)

											self.assertEqual(a_  ,  batch["""input_ids"""][0]		)
											self.assertEqual(a_  ,  batch["""labels"""][0]		)


							def _a      (							self		):
											# safety check on max_len default value so we are sure the test works
											a__				      =		self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			self.assertNotEqual(tokenizer.model_max_length  ,  42		)

        # Now let's start the test
											a__				      =		self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			# Isolate this from the other tests because we save additional tokens/etc
																			a__				      =		tempfile.mkdtemp()

																			a__				      =		""" He is very happy, UNwant\u00E9d,running"""
																			a__				      =		tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			tokenizer.save_pretrained(a_		)

																			a__				      =		tokenizer.__class__.from_pretrained(a_		)
																			a__				      =		after_tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			self.assertListEqual(a_  ,  a_		)

																			shutil.rmtree(a_		)

											a__				      =		self.get_tokenizers(model_max_length=42		)
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			# Isolate this from the other tests because we save additional tokens/etc
																			a__				      =		tempfile.mkdtemp()

																			a__				      =		""" He is very happy, UNwant\u00E9d,running"""
																			tokenizer.add_tokens(["""bim""", """bambam"""]		)
																			a__				      =		tokenizer.additional_special_tokens
																			additional_special_tokens.append("""new_additional_special_token"""		)
																			tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
																			a__				      =		tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			tokenizer.save_pretrained(a_		)

																			a__				      =		tokenizer.__class__.from_pretrained(a_		)
																			a__				      =		after_tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			self.assertListEqual(a_  ,  a_		)
																			self.assertIn("""new_additional_special_token"""  ,  after_tokenizer.additional_special_tokens		)
																			self.assertEqual(after_tokenizer.model_max_length  ,  42		)

																			a__				      =		tokenizer.__class__.from_pretrained(a_  ,  model_max_length=43		)
																			self.assertEqual(tokenizer.model_max_length  ,  43		)

																			shutil.rmtree(a_		)


							def _a      (							self		):
											a__				      =		[]
											if self.test_slow_tokenizer:
															tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

											if self.test_rust_tokenizer:
															tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

											for tokenizer_class, tokenizer_utils in tokenizer_list:
															with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer_utils.save_pretrained(a_		)

																			with open(os.path.join(a_  ,  """special_tokens_map.json"""		)  ,  encoding="""utf-8"""		) as json_file:
																							a__				      =		json.load(a_		)

																			with open(os.path.join(a_  ,  """tokenizer_config.json"""		)  ,  encoding="""utf-8"""		) as json_file:
																							a__				      =		json.load(a_		)

																			a__				      =		[F'''<extra_id_{i}>''' for i in range(125		)]

																			a__				      =		added_tokens_extra_ids + [
																			    """an_additional_special_token"""
																			]
																			a__				      =		added_tokens_extra_ids + [
																			    """an_additional_special_token"""
																			]

																			with open(os.path.join(a_  ,  """special_tokens_map.json"""		)  ,  """w"""  ,  encoding="""utf-8"""		) as outfile:
																							json.dump(a_  ,  a_		)
																			with open(os.path.join(a_  ,  """tokenizer_config.json"""		)  ,  """w"""  ,  encoding="""utf-8"""		) as outfile:
																							json.dump(a_  ,  a_		)

																			# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																			# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																			# "special_tokens_map.json" files
																			a__				      =		tokenizer_class.from_pretrained(
																			    a_  ,  )
																			self.assertIn(
																			    """an_additional_special_token"""  ,  tokenizer_without_change_in_init.additional_special_tokens		)
																			# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																			self.assertEqual(
																			    ["""an_additional_special_token"""]  ,  tokenizer_without_change_in_init.convert_ids_to_tokens(
																			        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]		)		)  ,  )

																			# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																			a__				      =		added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""  ,  lstrip=a_		)]
																			a__				      =		tokenizer_class.from_pretrained(
																			    a_  ,  additional_special_tokens=a_  ,  )

																			self.assertIn("""a_new_additional_special_token"""  ,  tokenizer.additional_special_tokens		)
																			self.assertEqual(
																			    ["""a_new_additional_special_token"""]  ,  tokenizer.convert_ids_to_tokens(
																			        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]		)		)  ,  )


							def _a      (							self		):
											a__				      =		[]
											if self.test_slow_tokenizer:
															tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

											if self.test_rust_tokenizer:
															tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

											for tokenizer_class, tokenizer_utils in tokenizer_list:
															with tempfile.TemporaryDirectory() as tmp_dir:
																			tokenizer_utils.save_pretrained(a_		)

																			a__				      =		tokenizer_class.from_pretrained(a_		)

																			self.assertTrue(tokenizer.decode([255]		) == """"""		)


							def _a      (							self		):
											pass


							def _a      (							self		):
											pass


							def _a      (							self		):
											pass


							def _a      (							self		):
											pass


							def _a      (							self		):
											# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
											# and special added tokens as tokens
											a__				      =		self.get_tokenizers(fast=a_  ,  do_lower_case=a_		)
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			a__				      =		["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
																			a__				      =		tokenizer.convert_tokens_to_string(a_		)

																			self.assertIsInstance(a_  ,  a_		)







							def _a      (							self		):
											a__				      =		self.get_tokenizers()
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			a__				      =		[
																			    """bos_token""",
																			    """eos_token""",
																			    """unk_token""",
																			    """sep_token""",
																			    """pad_token""",
																			    """cls_token""",
																			    """mask_token""",
																			]

																			a__				      =		0
																			a__				      =		tokenizer.convert_ids_to_tokens(
																			    a_  ,  skip_special_tokens=a_		)

																			for attr in attributes_list:
																							setattr(a_  ,  attr + """_id"""  ,  a_		)
																							self.assertEqual(getattr(a_  ,  a_		)  ,  a_		)
																							self.assertEqual(getattr(a_  ,  attr + """_id"""		)  ,  a_		)

																							setattr(a_  ,  attr + """_id"""  ,  a_		)
																							self.assertEqual(getattr(a_  ,  a_		)  ,  a_		)
																							self.assertEqual(getattr(a_  ,  attr + """_id"""		)  ,  a_		)

																			setattr(a_  ,  """additional_special_tokens_ids"""  ,  []		)
																			self.assertListEqual(getattr(a_  ,  """additional_special_tokens"""		)  ,  []		)
																			self.assertListEqual(getattr(a_  ,  """additional_special_tokens_ids"""		)  ,  []		)

																			setattr(a_  ,  """additional_special_tokens_ids"""  ,  [token_id_to_test_setters]		)
																			self.assertListEqual(getattr(a_  ,  """additional_special_tokens"""		)  ,  [token_to_test_setters]		)
																			self.assertListEqual(getattr(a_  ,  """additional_special_tokens_ids"""		)  ,  [token_id_to_test_setters]		)






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				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_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	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
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

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

