





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       (							__lowerCamelCase   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     GPTaTokenizer
       lowerCAmelCase__      		=     GPTaTokenizerFast
       lowerCAmelCase__      		=     True
       lowerCAmelCase__      		=     {"""add_prefix_space""": True}
       lowerCAmelCase__      		=     False




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         super().setUp()

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

         __snake_case:						Optional[int]   =						os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""vocab_file"""]		)
         __snake_case:						List[Any]   =						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 							UpperCAmelCase__ (  self :    Union[str, Any]							,				**A :    Optional[int]		):
         kwargs.update(self.special_tokens_map		)
         return GPTaTokenizer.from_pretrained(self.tmpdirname							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				**A :    List[str]		):
         kwargs.update(self.special_tokens_map		)
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname							,				**A		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    List[str]		):
         __snake_case:						str   =						"""lower newer"""
         __snake_case:						Dict   =						"""lower newer"""
         return input_text, output_text




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						Union[str, Any]   =						GPTaTokenizer(self.vocab_file							,				self.merges_file							,				**self.special_tokens_map		)
         __snake_case:						Optional[int]   =						"""lower newer"""
         __snake_case:						Optional[int]   =						["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         __snake_case:						Any   =						tokenizer.tokenize(A							,				add_prefix_space=A		)
         self.assertListEqual(A							,				A		)

         __snake_case:						Any   =						tokens + [tokenizer.unk_token]
         __snake_case:						Optional[int]   =						[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A		)							,				A		)




       def 							UpperCAmelCase__ (  self :    Any		):
         if not self.test_rust_tokenizer:
           return

         __snake_case:						int   =						self.get_tokenizer()
         __snake_case:						Optional[int]   =						self.get_rust_tokenizer(add_prefix_space=A		)

         __snake_case:						Tuple   =						"""lower newer"""

         # Testing tokenization
         __snake_case:						Any   =						tokenizer.tokenize(A							,				add_prefix_space=A		)
         __snake_case:						List[str]   =						rust_tokenizer.tokenize(A		)
         self.assertListEqual(A							,				A		)

         # Testing conversion to ids without special tokens
         __snake_case:						List[str]   =						tokenizer.encode(A							,				add_special_tokens=A							,				add_prefix_space=A		)
         __snake_case:						List[Any]   =						rust_tokenizer.encode(A							,				add_special_tokens=A		)
         self.assertListEqual(A							,				A		)

         # Testing conversion to ids with special tokens
         __snake_case:						Any   =						self.get_rust_tokenizer(add_prefix_space=A		)
         __snake_case:						Optional[Any]   =						tokenizer.encode(A							,				add_prefix_space=A		)
         __snake_case:						Tuple   =						rust_tokenizer.encode(A		)
         self.assertListEqual(A							,				A		)

         # Testing the unknown token
         __snake_case:						Tuple   =						tokens + [rust_tokenizer.unk_token]
         __snake_case:						Dict   =						[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A		)							,				A		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				*A :    Tuple							,				**A :    List[str]		):
         # 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 							UpperCAmelCase__ (  self :    str							,				A :    Union[str, Any]=15		):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
             __snake_case:						List[str]   =						self.rust_tokenizer_class.from_pretrained(A							,				**A		)

             # Simple input
             __snake_case:						str   =						"""This is a simple input"""
             __snake_case:						Any   =						["""This is a simple input 1""", """This is a simple input 2"""]
             __snake_case:						Dict   =						("""This is a simple input""", """This is a pair""")
             __snake_case:						int   =						[
                 ("""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 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Tuple   =						GPTaTokenizer.from_pretrained(self.tmpdirname							,				pad_token="""<pad>"""		)

         # Simple input
         __snake_case:						List[Any]   =						"""This is a simple input"""
         __snake_case:						Any   =						["""This is a simple input looooooooong""", """This is a simple input"""]
         __snake_case:						List[Any]   =						("""This is a simple input""", """This is a pair""")
         __snake_case:						int   =						[
             ("""This is a simple input loooooong""", """This is a simple input"""),
             ("""This is a simple pair loooooong""", """This is a simple pair"""),
         ]

         __snake_case:						Union[str, Any]   =						tokenizer.pad_token_id

         __snake_case:						Optional[int]   =						tokenizer(A							,				padding="""max_length"""							,				max_length=30							,				return_tensors="""np"""		)
         __snake_case:						Optional[int]   =						tokenizer(A							,				padding=A							,				truncate=A							,				return_tensors="""np"""		)
         __snake_case:						Tuple   =						tokenizer(*A							,				padding="""max_length"""							,				max_length=60							,				return_tensors="""np"""		)
         __snake_case:						Optional[int]   =						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 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						str   =						"""$$$"""
         __snake_case:						Optional[Any]   =						GPTaTokenizer.from_pretrained(self.tmpdirname							,				bos_token=A							,				add_bos_token=A		)

         __snake_case:						Any   =						"""This is a simple input"""
         __snake_case:						int   =						["""This is a simple input 1""", """This is a simple input 2"""]

         __snake_case:						Tuple   =						tokenizer.bos_token_id

         __snake_case:						Optional[Any]   =						tokenizer(A		)
         __snake_case:						Any   =						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		)		)

         __snake_case:						List[str]   =						tokenizer.decode(out_s.input_ids		)
         __snake_case:						Dict   =						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 							UpperCAmelCase__ (  self :    str		):
         pass






       def 							UpperCAmelCase__ (  self :    Tuple		):
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         __snake_case:						List[str]   =						[self.get_tokenizer(do_lower_case=A							,				add_bos_token=A		)]
         for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             __snake_case:						int   =						"""Encode this."""
             __snake_case:						str   =						"""This one too please."""
             __snake_case:						Tuple   =						tokenizer.encode(A							,				add_special_tokens=A		)
             encoded_sequence += tokenizer.encode(A							,				add_special_tokens=A		)
             __snake_case:						List[str]   =						tokenizer.encode_plus(
                 A							,				A							,				add_special_tokens=A							,				return_special_tokens_mask=A							,				)
             __snake_case:						List[Any]   =						encoded_sequence_dict["""input_ids"""]
             __snake_case:						Dict   =						encoded_sequence_dict["""special_tokens_mask"""]
             self.assertEqual(len(A		)							,				len(A		)		)

             __snake_case:						int   =						[
                 (x if not special_tokens_mask[i] else None) for i, x in enumerate(A		)
             ]
             __snake_case:						List[str]   =						[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 							UpperCAmelCase__ (  self :    List[Any]		):
         # 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

         __snake_case:						List[Any]   =						AutoTokenizer.from_pretrained("""facebook/opt-350m"""							,				from_slow=A		)
         __snake_case:						List[Any]   =						"""A photo of a cat"""

         __snake_case:						Union[str, Any]   =						tokenizer.encode(
             A							,				)
         self.assertEqual(A							,				[2, 250, 1_345, 9, 10, 4_758]		)
         tokenizer.save_pretrained("""test_opt"""		)

         __snake_case:						str   =						AutoTokenizer.from_pretrained("""./test_opt"""		)
         __snake_case:						Union[str, Any]   =						tokenizer.encode(
             A							,				)
         self.assertEqual(A							,				[2, 250, 1_345, 9, 10, 4_758]		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[str]   =						AutoTokenizer.from_pretrained("""facebook/opt-350m"""							,				use_slow=A		)
         __snake_case:						Optional[int]   =						"""A photo of a cat"""

         __snake_case:						Optional[Any]   =						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 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained("""facebook/opt-350m"""							,				from_slow=A		)

         __snake_case:						Any   =						"""bos"""
         __snake_case:						Union[str, Any]   =						tokenizer.get_vocab()["""bos"""]

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













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






