
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:       int   		=       logging.get_logger(__name__)

_A			:       Optional[int]   		=       {
    """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
    """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
    """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
    """funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
    """funnel-transformer/intermediate""": (
        """https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
    ),
    """funnel-transformer/intermediate-base""": (
        """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
    ),
    """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
    """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
    """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
    """funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				"""funnel"""
   __lowerCAmelCase            =				{
       """hidden_size""": """d_model""",
       """num_attention_heads""": """n_head""",
   }
   def __init__(			self	,    _a=30_522	,    _a=[4, 4, 4]	,    _a=None	,    _a=2	,    _a=768	,    _a=12	,    _a=64	,    _a=3_072	,    _a="gelu_new"	,    _a=0.1	,    _a=0.1	,    _a=0.0	,    _a=0.1	,    _a=None	,    _a=1E-9	,    _a="mean"	,    _a="relative_shift"	,    _a=True	,    _a=True	,    _a=True	,    **_a	,    ):
          lowercase    :				Tuple       		=							vocab_size
          lowercase    :				int       		=							block_sizes
          lowercase    :				Optional[Any]       		=							[1] * len(_a				) if block_repeats is None else block_repeats
          assert len(_a				) == len(
              self.block_repeats				), "`block_sizes` and `block_repeats` should have the same length."
          lowercase    :				str       		=							num_decoder_layers
          lowercase    :				int       		=							d_model
          lowercase    :				str       		=							n_head
          lowercase    :				int       		=							d_head
          lowercase    :				Optional[int]       		=							d_inner
          lowercase    :				Any       		=							hidden_act
          lowercase    :				Union[str, Any]       		=							hidden_dropout
          lowercase    :				Union[str, Any]       		=							attention_dropout
          lowercase    :				Tuple       		=							activation_dropout
          lowercase    :				List[Any]       		=							initializer_range
          lowercase    :				Optional[Any]       		=							initializer_std
          lowercase    :				List[str]       		=							layer_norm_eps
          assert pooling_type in [
              "mean",
              "max",
          ], f"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
          lowercase    :				int       		=							pooling_type
          assert attention_type in [
              "relative_shift",
              "factorized",
          ], f"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
          lowercase    :				List[Any]       		=							attention_type
          lowercase    :				Optional[int]       		=							separate_cls
          lowercase    :				List[Any]       		=							truncate_seq
          lowercase    :				Optional[int]       		=							pool_q_only

          super().__init__(**_a				)
   @property
   def 			__magic_name__			(			self				):
          return sum(self.block_sizes				)
   @num_hidden_layers.setter
   def 			__magic_name__			(			self	,    _a				):
          raise NotImplementedError(
              "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."				)
   @property
   def 			__magic_name__			(			self				):
          return len(self.block_sizes				)

   @num_blocks.setter
   def 			__magic_name__			(			self	,    _a				):
          raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`."				)




"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
_A			:       Optional[int]   		=       """
Human: <<task>>

Assistant: """


_A			:       List[Any]   		=       """huggingface-tools/default-prompts"""
_A			:       Optional[int]   		=       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       List[Any]     , __snake_case				:       Dict="run"  ) ->				Union[str, Any]:
       if prompt_or_repo_id is None:
              lowercase    :				List[Any]       		=							DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search("\\s"     , __snake_case  ) is not None:
              return prompt_or_repo_id

       lowercase    :				Optional[int]       		=							cached_file(
           __snake_case     , PROMPT_FILES[mode]     , repo_type="dataset"     , user_agent={"agent": agent_name}  )
       with open(__snake_case     , "r"     , encoding="utf-8"  ) as f:
              return f.read()


