

"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a					:List[str]    = logging.get_logger(__name__)

a					:int    = {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """gptj"""
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = {
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }

     def __init__(						self	,    _a=50_400	,    _a=2_048	,    _a=4_096	,    _a=28	,    _a=16	,    _a=64	,    _a=None	,    _a="gelu_new"	,    _a=0.0	,    _a=0.0	,    _a=0.0	,    _a=1E-5	,    _a=0.02	,    _a=True	,    _a=50_256	,    _a=50_256	,    _a=False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	n_positions
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	n_embd
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	n_layer
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	n_head
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	n_inner
           SCREAMING_SNAKE_CASE__   :      str      		=	rotary_dim
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	activation_function
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	resid_pdrop
           SCREAMING_SNAKE_CASE__   :      Dict      		=	embd_pdrop
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	attn_pdrop
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      str      		=	bos_token_id
           SCREAMING_SNAKE_CASE__   :      int      		=	eos_token_id

           super().__init__(
               bos_token_id=__A	,    eos_token_id=__A	,    tie_word_embeddings=__A	,    **__A  )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a = "default"	,    _a = None	,    _a = False	,    )  ->      List[str]:


           """simple docstring"""


           super().__init__(__A	,    task=__A	,    patching_specs=__A	,    use_past=__A  )
           if not getattr(self._config	,    """pad_token_id"""	,    __A  ):
                 # TODO: how to do that better?
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}  )
           if self.use_past:
                 self.fill_with_past_key_values_(__A	,    direction="""inputs"""  )
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch""", 1: """past_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{0: """batch""", 1: """sequence"""}

           return common_inputs

     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self._config.n_layer

     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self._config.n_head

     def 					_a			(						self	,    _a	,    _a = -1	,    _a = -1	,    _a = False	,    _a = None	,    )  ->      Mapping[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	super(__A	,    self  ).generate_dummy_inputs(
               __A	,    batch_size=__A	,    seq_length=__A	,    is_pair=__A	,    framework=__A  )

           # We need to order the input in the way they appears in the forward()
           SCREAMING_SNAKE_CASE__   :      str      		=	OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}  )

           # Need to add the past_keys
           if self.use_past:
                 if not is_torch_available():
                       raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
                 else:
                       import torch

                       SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	common_inputs["""input_ids"""].shape
                       # Not using the same length for past_key_values
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	seqlen + 2
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	(
                           batch,
                           self.num_attention_heads,
                           past_key_values_length,
                           self._config.hidden_size // self.num_attention_heads,
                       )
                       SCREAMING_SNAKE_CASE__   :      Any      		=	[
                           (torch.zeros(__A  ), torch.zeros(__A  )) for _ in range(self.num_layers  )
                       ]

           SCREAMING_SNAKE_CASE__   :      int      		=	common_inputs["""attention_mask"""]
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      int      		=	ordered_inputs["""attention_mask"""].dtype
                 SCREAMING_SNAKE_CASE__   :      Any      		=	torch.cat(
                     [ordered_inputs["""attention_mask"""], torch.ones(__A	,    __A	,    dtype=__A  )]	,    dim=1  )

           return ordered_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13







"""simple docstring"""


import re
from pathlib import Path
from unittest import TestCase

import pytest

@pytest.mark.integration
class      __a							(UpperCamelCase_):






     '''simple docstring'''

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


           """simple docstring"""


           with open(_a	,    encoding="""utf-8"""  ) as input_file:
                 SCREAMING_SNAKE_CASE__   :      str      		=	re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	input_file.read()
                 SCREAMING_SNAKE_CASE__   :      str      		=	regexp.search(_a  )

           return match

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


           """simple docstring"""


           with open(_a	,    encoding="""utf-8"""  ) as input_file:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""	,    re.DOTALL  )
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	input_file.read()
                 # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	regexp.finditer(_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[match for match in matches if match is not None and match.group(1  ) is not None]
           return matches[0] if matches else None

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Path("""./datasets"""  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	list(dataset_paths.absolute().glob("""**/*.py"""  )  )

           for dataset in dataset_files:
                 if self._no_encoding_on_file_open(str(_a  )  ):
                       raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''  )





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Path("""./datasets"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	list(dataset_paths.absolute().glob("""**/*.py"""  )  )

           for dataset in dataset_files:
                 if self._no_print_statements(str(_a  )  ):
                       raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''  )




