




"""simple docstring"""


import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf






class 					lowercase(     enum.Enum ):







 '''simple docstring'''





 lowercase__ 					=					0
 lowercase__ 					=					1
 lowercase__ 					=					2







@add_end_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "


 def __init__(				self:		List[Any],      *a_:		int,      **a_:		str		):




   '''simple docstring'''



   super().__init__(*a_,      **a_		)
   self.check_model_type(
       TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING		)
   if "prefix" not in self._preprocess_params:
     # This is very specific. The logic is quite complex and needs to be done
     # as a "default".
     # It also defines both some preprocess_kwargs and generate_kwargs
     # which is why we cannot put them in their respective methods.
     _snake_case							:		str								=  None
     if self.model.config.prefix is not None:
       _snake_case							:		Optional[Any]								=  self.model.config.prefix
     if prefix is None and self.model.__class__.__name__ in [
         "XLNetLMHeadModel",
         "TransfoXLLMHeadModel",
         "TFXLNetLMHeadModel",
         "TFTransfoXLLMHeadModel",
     ]:
       # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
       _snake_case							:		Any								=  self.XL_PREFIX
     if prefix is not None:
       # Recalculate some generate_kwargs linked to prefix.
       _snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  self._sanitize_parameters(prefix=a_,      **self._forward_params		)
       _snake_case							:		Dict								=  {**self._preprocess_params, **preprocess_params}
       _snake_case							:		Dict								=  {**self._forward_params, **forward_params}


 def  UpperCamelCase_   (				self:		Any,      a_:		Optional[int]=None,      a_:		str=None,      a_:		Optional[int]=None,      a_:		str=None,      a_:		Dict=None,      a_:		Tuple=None,      a_:		Tuple=None,      a_:		List[Any]=None,      **a_:		int,      ):




   '''simple docstring'''



   _snake_case							:		str								=  {}
   if prefix is not None:
     _snake_case							:		List[Any]								=  prefix
   if prefix:
     _snake_case							:		List[Any]								=  self.tokenizer(
         a_,      padding=a_,      add_special_tokens=a_,      return_tensors=self.framework		)
     _snake_case							:		List[Any]								=  prefix_inputs["""input_ids"""].shape[-1]

   if handle_long_generation is not None:
     if handle_long_generation not in {"hole"}:
       raise ValueError(
           f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
           """ [None, 'hole']"""		)
     _snake_case							:		List[Any]								=  handle_long_generation

   preprocess_params.update(a_		)
   _snake_case							:		Optional[Any]								=  generate_kwargs

   _snake_case							:		Tuple								=  {}
   if return_full_text is not None and return_type is None:
     if return_text is not None:
       raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""		)
     if return_tensors is not None:
       raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""		)
     _snake_case							:		int								=  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
   if return_tensors is not None and return_type is None:
     if return_text is not None:
       raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""		)
     _snake_case							:		Union[str, Any]								=  ReturnType.TENSORS
   if return_type is not None:
     _snake_case							:		List[str]								=  return_type
   if clean_up_tokenization_spaces is not None:
     _snake_case							:		str								=  clean_up_tokenization_spaces

   if stop_sequence is not None:
     _snake_case							:		Dict								=  self.tokenizer.encode(a_,      add_special_tokens=a_		)
     if len(a_		) > 1:
       warnings.warn(
           """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
           """ the stop sequence will be used as the stop sequence string in the interim."""		)
     _snake_case							:		Optional[Any]								=  stop_sequence_ids[0]

   return preprocess_params, forward_params, postprocess_params


 def  UpperCamelCase_   (				self:		Dict,      *a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
     kwargs.update({"""add_space_before_punct_symbol""": True}		)

   return super()._parse_and_tokenize(*a_,      **a_		)


 def __call__(				self:		List[Any],      a_:		Dict,      **a_:		List[str]		):




   '''simple docstring'''



   return super().__call__(a_,      **a_		)


 def  UpperCamelCase_   (				self:		Any,      a_:		int,      a_:		List[Any]="",      a_:		Tuple=None,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.tokenizer(
       prefix + prompt_text,      padding=a_,      add_special_tokens=a_,      return_tensors=self.framework		)
   _snake_case							:		int								=  prompt_text

   if handle_long_generation == "hole":
     _snake_case							:		Union[str, Any]								=  inputs["""input_ids"""].shape[-1]
     if "max_new_tokens" in generate_kwargs:
       _snake_case							:		Union[str, Any]								=  generate_kwargs["""max_new_tokens"""]
     else:
       _snake_case							:		Union[str, Any]								=  generate_kwargs.get("""max_length""",      self.model.config.max_length		) - cur_len
       if new_tokens < 0:
         raise ValueError("""We cannot infer how many new tokens are expected"""		)
     if cur_len + new_tokens > self.tokenizer.model_max_length:
       _snake_case							:		List[Any]								=  self.tokenizer.model_max_length - new_tokens
       if keep_length <= 0:
         raise ValueError(
             """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
             """ models max length"""		)

       _snake_case							:		int								=  inputs["""input_ids"""][:, -keep_length:]
       if "attention_mask" in inputs:
         _snake_case							:		List[str]								=  inputs["""attention_mask"""][:, -keep_length:]

   return inputs


 def  UpperCamelCase_   (				self:		int,      a_:		Any,      **a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  model_inputs["""input_ids"""]
   _snake_case							:		str								=  model_inputs.get("""attention_mask""",      a_		)
   # Allow empty prompts
   if input_ids.shape[1] == 0:
     _snake_case							:		Any								=  None
     _snake_case							:		int								=  None
     _snake_case							:		Union[str, Any]								=  1
   else:
     _snake_case							:		List[Any]								=  input_ids.shape[0]
   _snake_case							:		Optional[int]								=  model_inputs.pop("""prompt_text"""		)

   # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
   # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
   _snake_case							:		Any								=  generate_kwargs.pop("""prefix_length""",      0		)
   if prefix_length > 0:
     _snake_case							:		str								=  """max_new_tokens""" in generate_kwargs or (
         """generation_config""" in generate_kwargs
         and generate_kwargs["""generation_config"""].max_new_tokens is not None
     )
     if not has_max_new_tokens:
       _snake_case							:		Dict								=  generate_kwargs.get("""max_length"""		) or self.model.config.max_length
       generate_kwargs["max_length"] += prefix_length
     _snake_case							:		Any								=  """min_new_tokens""" in generate_kwargs or (
         """generation_config""" in generate_kwargs
         and generate_kwargs["""generation_config"""].min_new_tokens is not None
     )
     if not has_min_new_tokens and "min_length" in generate_kwargs:
       generate_kwargs["min_length"] += prefix_length

        # BS x SL
   _snake_case							:		Dict								=  self.model.generate(input_ids=a_,      attention_mask=a_,      **a_		)
   _snake_case							:		Union[str, Any]								=  generated_sequence.shape[0]
   if self.framework == "pt":
     _snake_case							:		List[Any]								=  generated_sequence.reshape(a_,      out_b // in_b,      *generated_sequence.shape[1:]		)
   elif self.framework == "tf":
     _snake_case							:		Optional[int]								=  tf.reshape(a_,      (in_b, out_b // in_b, *generated_sequence.shape[1:])		)
   return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Dict,      a_:		Any=ReturnType.FULL_TEXT,      a_:		Union[str, Any]=True		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  model_outputs["""generated_sequence"""][0]
   _snake_case							:		Any								=  model_outputs["""input_ids"""]
   _snake_case							:		int								=  model_outputs["""prompt_text"""]
   _snake_case							:		Optional[Any]								=  generated_sequence.numpy().tolist()
   _snake_case							:		str								=  []
   for sequence in generated_sequence:
     if return_type == ReturnType.TENSORS:
       _snake_case							:		Union[str, Any]								=  {"""generated_token_ids""": sequence}
     elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
       # Decode text
       _snake_case							:		List[str]								=  self.tokenizer.decode(
           a_,      skip_special_tokens=a_,      clean_up_tokenization_spaces=a_,      )

       # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
       if input_ids is None:
         _snake_case							:		str								=  0
       else:
         _snake_case							:		Optional[Any]								=  len(
             self.tokenizer.decode(
                 input_ids[0],      skip_special_tokens=a_,      clean_up_tokenization_spaces=a_,      )		)

       if return_type == ReturnType.FULL_TEXT:
         _snake_case							:		Optional[int]								=  prompt_text + text[prompt_length:]
       else:
         _snake_case							:		int								=  text[prompt_length:]

       _snake_case							:		List[Any]								=  {"""generated_text""": all_text}
     records.append(a_		)

   return records








"""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 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					CodeGenTokenizer
 lowercase__ 					=					CodeGenTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					{"add_prefix_space": True}
 lowercase__ 					=					False


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   super().setUp()

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

   _snake_case							:		Optional[int]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""vocab_file"""]		)
   _snake_case							:		Optional[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:		Any,      **a_:		int		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Any,      **a_:		str		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  """lower newer"""
   _snake_case							:		Tuple								=  """lower newer"""
   return input_text, output_text


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  CodeGenTokenizer(self.vocab_file,      self.merges_file,      **self.special_tokens_map		)
   _snake_case							:		Optional[Any]								=  """lower newer"""
   _snake_case							:		Optional[int]								=  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
   _snake_case							:		int								=  tokenizer.tokenize(a_,      add_prefix_space=a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		str								=  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:		Union[str, Any]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		int								=  self.get_tokenizer()
   _snake_case							:		int								=  self.get_rust_tokenizer(add_prefix_space=a_		)

   _snake_case							:		Dict								=  """lower newer"""

   # Testing tokenization
   _snake_case							:		Dict								=  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							:		Optional[Any]								=  tokenizer.encode(a_,      add_special_tokens=a_,      add_prefix_space=a_		)
   _snake_case							:		Tuple								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

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

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


 def  UpperCamelCase_   (				self:		Dict,      *a_:		Dict,      **a_:		int		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		int,      a_:		List[Any]=15		):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		List[Any]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)

       # Simple input
       _snake_case							:		Any								=  """This is a simple input"""
       _snake_case							:		Optional[int]								=  ["""This is a simple input 1""", """This is a simple input 2"""]
       _snake_case							:		Optional[int]								=  ("""This is a simple input""", """This is a pair""")
       _snake_case							:		Optional[Any]								=  [
           ("""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:		Optional[Any]		):




   '''simple docstring'''



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

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

   _snake_case							:		str								=  tokenizer.pad_token_id

   _snake_case							:		Optional[int]								=  tokenizer(a_,      padding="""max_length""",      max_length=30,      return_tensors="""np"""		)
   _snake_case							:		Dict								=  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[Any]								=  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:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  """$$$"""
   _snake_case							:		List[Any]								=  CodeGenTokenizer.from_pretrained(self.tmpdirname,      bos_token=a_,      add_bos_token=a_		)

   _snake_case							:		str								=  """This is a simple input"""
   _snake_case							:		int								=  ["""This is a simple input 1""", """This is a simple input 2"""]

   _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id

   _snake_case							:		Tuple								=  tokenizer(a_		)
   _snake_case							:		Optional[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							:		Optional[int]								=  tokenizer.decode(out_s.input_ids		)
   _snake_case							:		int								=  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  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""		)

   _snake_case							:		Dict								=  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
   _snake_case							:		Union[str, Any]								=  """\nif len_a > len_b:      result = a\nelse:      result = b"""

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


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   pass


