

"""simple docstring"""


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = CodeGenTokenizer
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = CodeGenTokenizerFast
     _SCREAMING_SNAKE_CASE						:Tuple       = True
     _SCREAMING_SNAKE_CASE						:Optional[int]       = {"""add_prefix_space""": True}
     _SCREAMING_SNAKE_CASE						:int       = False

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           super().setUp()

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           SCREAMING_SNAKE_CASE__   :      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 					_a			(						self	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           kwargs.update(self.special_tokens_map  )
           return CodeGenTokenizer.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           kwargs.update(self.special_tokens_map  )
           return CodeGenTokenizerFast.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    _a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""lower newer"""
           return input_text, output_text

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	CodeGenTokenizer(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.tokenize(_a	,    add_prefix_space=_a  )
           self.assertListEqual(_a	,    _a  )

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

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer(add_prefix_space=_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	"""lower newer"""

           # Testing tokenization
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.tokenize(_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	rust_tokenizer.tokenize(_a  )
           self.assertListEqual(_a	,    _a  )

           # Testing conversion to ids without special tokens
           SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer.encode(_a	,    add_special_tokens=_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rust_tokenizer.encode(_a	,    add_special_tokens=_a  )
           self.assertListEqual(_a	,    _a  )

           # Testing conversion to ids with special tokens
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_rust_tokenizer(add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.encode(_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	rust_tokenizer.encode(_a  )
           self.assertListEqual(_a	,    _a  )

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

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           pass

     def 					_a			(						self	,    _a=15  )  ->      Union[str, Any]:


           """simple docstring"""


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

                       # Simple input
                       SCREAMING_SNAKE_CASE__   :      int      		=	"""This is a simple input"""
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""This is a simple input 1""", """This is a simple input 2"""]
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	("""This is a simple input""", """This is a pair""")
                       SCREAMING_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 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	CodeGenTokenizer.from_pretrained(self.tmpdirname	,    pad_token="""<pad>"""  )

           # Simple input
           SCREAMING_SNAKE_CASE__   :      Dict      		=	"""This is a simple input"""
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""This is a simple input looooooooong""", """This is a simple input"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	("""This is a simple input""", """This is a pair""")
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[
               ("""This is a simple input loooooong""", """This is a simple input"""),
               ("""This is a simple pair loooooong""", """This is a simple pair"""),
           ]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.pad_token_id

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer(_a	,    padding="""max_length"""	,    max_length=30	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer(_a	,    padding=_a	,    truncate=_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer(*_a	,    padding="""max_length"""	,    max_length=60	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""$$$"""
           SCREAMING_SNAKE_CASE__   :      Dict      		=	CodeGenTokenizer.from_pretrained(self.tmpdirname	,    bos_token=_a	,    add_bos_token=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""This is a simple input"""
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""This is a simple input 1""", """This is a simple input 2"""]

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tokenizer.bos_token_id

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer(_a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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  )  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.decode(out_s.input_ids  )
           SCREAMING_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  )  )

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""\nif len_a > len_b:      result = a\nelse:      result = b"""

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.encode(_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	["""^#""", re.escape("""<|endoftext|>"""  ), """^'''""", """^\"\"\"""", """\n\n\n"""]
           SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer.decode(_a	,    truncate_before_pattern=_a  )
           self.assertEqual(_a	,    _a  )





     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           pass







"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




