



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					GPTaTokenizer
							UpperCamelCase__				:      Dict         =					GPTaTokenizerFast
							UpperCamelCase__				:      List[Any]         =					True
							UpperCamelCase__				:      int         =					{"""add_prefix_space""": True}
							UpperCamelCase__				:      Optional[Any]         =					False


							def _a      (							self		):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											a__				      =		[
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											    """<|endoftext|>""",
											]
											a__				      =		dict(zip(a_  ,  range(len(a_		)		)		)		)
											a__				      =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											a__				      =		{"""unk_token""": """<unk>"""}

											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
											a__				      =		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(a_		) + """\n"""		)
											with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write("""\n""".join(a_		)		)


							def _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return GPTaTokenizer.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return GPTaTokenizerFast.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  a_		):
											a__				      =		"""lower newer"""
											a__				      =		"""lower newer"""
											return input_text, output_text


							def _a      (							self		):
											a__				      =		GPTaTokenizer(self.vocab_file  ,  self.merges_file  ,  **self.special_tokens_map		)
											a__				      =		"""lower newer"""
											a__				      =		["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
											a__				      =		tokenizer.tokenize(a_  ,  add_prefix_space=a_		)
											self.assertListEqual(a_  ,  a_		)

											a__				      =		tokens + [tokenizer.unk_token]
											a__				      =		[14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		)  ,  a_		)


							def _a      (							self		):
											if not self.test_rust_tokenizer:
															return

											a__				      =		self.get_tokenizer()
											a__				      =		self.get_rust_tokenizer(add_prefix_space=a_		)

											a__				      =		"""lower newer"""

											# Testing tokenization
											a__				      =		tokenizer.tokenize(a_  ,  add_prefix_space=a_		)
											a__				      =		rust_tokenizer.tokenize(a_		)
											self.assertListEqual(a_  ,  a_		)

											# Testing conversion to ids without special tokens
											a__				      =		tokenizer.encode(a_  ,  add_special_tokens=a_  ,  add_prefix_space=a_		)
											a__				      =		rust_tokenizer.encode(a_  ,  add_special_tokens=a_		)
											self.assertListEqual(a_  ,  a_		)

											# Testing conversion to ids with special tokens
											a__				      =		self.get_rust_tokenizer(add_prefix_space=a_		)
											a__				      =		tokenizer.encode(a_  ,  add_prefix_space=a_		)
											a__				      =		rust_tokenizer.encode(a_		)
											self.assertListEqual(a_  ,  a_		)

											# Testing the unknown token
											a__				      =		tokens + [rust_tokenizer.unk_token]
											a__				      =		[14, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_		)  ,  a_		)


							def _a      (							self  ,  *a_  ,  **a_		):
											# It's very difficult to mix/test pretokenization with byte-level
											# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
											pass


							def _a      (							self  ,  a_=15		):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																			a__				      =		self.rust_tokenizer_class.from_pretrained(a_  ,  **a_		)

																			# Simple input
																			a__				      =		"""This is a simple input"""
																			a__				      =		["""This is a simple input 1""", """This is a simple input 2"""]
																			a__				      =		("""This is a simple input""", """This is a pair""")
																			a__				      =		[
																			    ("""This is a simple input 1""", """This is a simple input 2"""),
																			    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																			]

																			# Simple input tests
																			self.assertRaises(a_  ,  tokenizer_r.encode  ,  a_  ,  max_length=a_  ,  padding="""max_length"""		)

																			# Simple input
																			self.assertRaises(a_  ,  tokenizer_r.encode_plus  ,  a_  ,  max_length=a_  ,  padding="""max_length"""		)

																			# Simple input
																			self.assertRaises(
																			    a_  ,  tokenizer_r.batch_encode_plus  ,  a_  ,  max_length=a_  ,  padding="""max_length"""  ,  )

																			# Pair input
																			self.assertRaises(a_  ,  tokenizer_r.encode  ,  a_  ,  max_length=a_  ,  padding="""max_length"""		)

																			# Pair input
																			self.assertRaises(a_  ,  tokenizer_r.encode_plus  ,  a_  ,  max_length=a_  ,  padding="""max_length"""		)

																			# Pair input
																			self.assertRaises(
																			    a_  ,  tokenizer_r.batch_encode_plus  ,  a_  ,  max_length=a_  ,  padding="""max_length"""  ,  )


							def _a      (							self		):
											a__				      =		GPTaTokenizer.from_pretrained(self.tmpdirname  ,  pad_token="""<pad>"""		)

											# Simple input
											a__				      =		"""This is a simple input"""
											a__				      =		["""This is a simple input looooooooong""", """This is a simple input"""]
											a__				      =		("""This is a simple input""", """This is a pair""")
											a__				      =		[
											    ("""This is a simple input loooooong""", """This is a simple input"""),
											    ("""This is a simple pair loooooong""", """This is a simple pair"""),
											]

											a__				      =		tokenizer.pad_token_id

											a__				      =		tokenizer(a_  ,  padding="""max_length"""  ,  max_length=30  ,  return_tensors="""np"""		)
											a__				      =		tokenizer(a_  ,  padding=a_  ,  truncate=a_  ,  return_tensors="""np"""		)
											a__				      =		tokenizer(*a_  ,  padding="""max_length"""  ,  max_length=60  ,  return_tensors="""np"""		)
											a__				      =		tokenizer(a_  ,  padding=a_  ,  truncate=a_  ,  return_tensors="""np"""		)

											# s
											# test single string max_length padding
											self.assertEqual(out_s["""input_ids"""].shape[-1]  ,  30		)
											self.assertTrue(pad_token_id in out_s["""input_ids"""]		)
											self.assertTrue(0 in out_s["""attention_mask"""]		)

											# s2
											# test automatic padding
											self.assertEqual(out_sa["""input_ids"""].shape[-1]  ,  33		)
											# long slice doesn't have padding
											self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]		)
											self.assertFalse(0 in out_sa["""attention_mask"""][0]		)
											# short slice does have padding
											self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]		)
											self.assertTrue(0 in out_sa["""attention_mask"""][1]		)

											# p
											# test single pair max_length padding
											self.assertEqual(out_p["""input_ids"""].shape[-1]  ,  60		)
											self.assertTrue(pad_token_id in out_p["""input_ids"""]		)
											self.assertTrue(0 in out_p["""attention_mask"""]		)

											# p2
											# test automatic padding pair
											self.assertEqual(out_pa["""input_ids"""].shape[-1]  ,  52		)
											# long slice pair doesn't have padding
											self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]		)
											self.assertFalse(0 in out_pa["""attention_mask"""][0]		)
											# short slice pair does have padding
											self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]		)
											self.assertTrue(0 in out_pa["""attention_mask"""][1]		)


							def _a      (							self		):
											a__				      =		"""$$$"""
											a__				      =		GPTaTokenizer.from_pretrained(self.tmpdirname  ,  bos_token=a_  ,  add_bos_token=a_		)

											a__				      =		"""This is a simple input"""
											a__				      =		["""This is a simple input 1""", """This is a simple input 2"""]

											a__				      =		tokenizer.bos_token_id

											a__				      =		tokenizer(a_		)
											a__				      =		tokenizer(a_		)

											self.assertEqual(out_s.input_ids[0]  ,  a_		)
											self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids		)		)

											a__				      =		tokenizer.decode(out_s.input_ids		)
											a__				      =		tokenizer.batch_decode(out_sa.input_ids		)

											self.assertEqual(decode_s.split()[0]  ,  a_		)
											self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa		)		)


							def _a      (							self		):
											pass







							def _a      (							self		):
											# TODO: change to self.get_tokenizers() when the fast version is implemented
											a__				      =		[self.get_tokenizer(do_lower_case=a_  ,  add_bos_token=a_		)]
											for tokenizer in tokenizers:
															with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																			a__				      =		"""Encode this."""
																			a__				      =		"""This one too please."""
																			a__				      =		tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			encoded_sequence += tokenizer.encode(a_  ,  add_special_tokens=a_		)
																			a__				      =		tokenizer.encode_plus(
																			    a_  ,  a_  ,  add_special_tokens=a_  ,  return_special_tokens_mask=a_  ,  )
																			a__				      =		encoded_sequence_dict["""input_ids"""]
																			a__				      =		encoded_sequence_dict["""special_tokens_mask"""]
																			self.assertEqual(len(a_		)  ,  len(a_		)		)

																			a__				      =		[
																			    (x if not special_tokens_mask[i] else None) for i, x in enumerate(a_		)
																			]
																			a__				      =		[x for x in filtered_sequence if x is not None]
																			self.assertEqual(a_  ,  a_		)




@require_tokenizers
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											# More context:
											# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
											# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
											# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

											a__				      =		AutoTokenizer.from_pretrained("""facebook/opt-350m"""  ,  from_slow=a_		)
											a__				      =		"""A photo of a cat"""

											a__				      =		tokenizer.encode(
											    a_  ,  )
											self.assertEqual(a_  ,  [2, 250, 1_345, 9, 10, 4_758]		)
											tokenizer.save_pretrained("""test_opt"""		)

											a__				      =		AutoTokenizer.from_pretrained("""./test_opt"""		)
											a__				      =		tokenizer.encode(
											    a_  ,  )
											self.assertEqual(a_  ,  [2, 250, 1_345, 9, 10, 4_758]		)


							def _a      (							self		):
											a__				      =		AutoTokenizer.from_pretrained("""facebook/opt-350m"""  ,  use_slow=a_		)
											a__				      =		"""A photo of a cat"""

											a__				      =		tokenizer.encode(
											    a_  ,  )
											# Same as above
											self.assertEqual(a_  ,  [2, 250, 1_345, 9, 10, 4_758]		)







							@unittest.skip("""This test is failing because of a bug in the fast tokenizer"""		)
							def _a      (							self		):
											a__				      =		AutoTokenizer.from_pretrained("""facebook/opt-350m"""  ,  from_slow=a_		)

											a__				      =		"""bos"""
											a__				      =		tokenizer.get_vocab()["""bos"""]

											a__				      =		"""A photo of a cat"""
											a__				      =		tokenizer.encode(
											    a_  ,  )
											# We changed the bos token
											self.assertEqual(a_  ,  [31_957, 250, 1_345, 9, 10, 4_758]		)
											tokenizer.save_pretrained("""./tok"""		)
											a__				      =		AutoTokenizer.from_pretrained("""./tok"""		)
											self.assertTrue(tokenizer.is_fast		)
											a__				      =		tokenizer.encode(
											    a_  ,  )
											self.assertEqual(a_  ,  [31_957, 250, 1_345, 9, 10, 4_758]		)






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

