


from typing import Mapping

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


snake_case							: Optional[int]      =   logging.get_logger(__name__)

snake_case							: Any      =   {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class  snake_case_     (a__   ):
					UpperCAmelCase__      :					int					   =    "umt5"
					UpperCAmelCase__      :					Optional[Any]					   =    ["past_key_values"]
					def __init__(   self     :Optional[int]    ,__snake_case     :str=25_01_12    ,__snake_case     :Optional[Any]=5_12    ,__snake_case     :int=64    ,__snake_case     :Optional[int]=10_24    ,__snake_case     :Dict=8    ,__snake_case     :str=None    ,__snake_case     :List[Any]=6    ,__snake_case     :Any=32    ,__snake_case     :str=1_28    ,__snake_case     :int=0.1    ,__snake_case     :int=1E-6    ,__snake_case     :Optional[Any]=1.0    ,__snake_case     :Any="gated-gelu"    ,__snake_case     :List[str]=True    ,__snake_case     :List[str]=True    ,__snake_case     :Optional[int]="T5Tokenizer"    ,__snake_case     :Optional[int]=True    ,__snake_case     :List[str]=0    ,__snake_case     :Tuple=1    ,__snake_case     :str=0    ,**__snake_case     :Optional[int]    ,)      ->					str:
												super().__init__(
												    is_encoder_decoder=lowerCamelCase_    ,tokenizer_class=lowerCamelCase_    ,tie_word_embeddings=lowerCamelCase_    ,pad_token_id=lowerCamelCase_    ,eos_token_id=lowerCamelCase_    ,decoder_start_token_id=lowerCamelCase_    ,**lowerCamelCase_    ,)
												a__    =  vocab_size
												a__    =  d_model
												a__    =  d_kv
												a__    =  d_ff
												a__    =  num_layers
												a__    =  (
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												a__    =  num_heads
												a__    =  relative_attention_num_buckets
												a__    =  relative_attention_max_distance
												a__    =  dropout_rate
												a__    =  layer_norm_epsilon
												a__    =  initializer_factor
												a__    =  feed_forward_proj
												a__    =  use_cache

												a__    =  self.feed_forward_proj.split('-'   )
												a__    =  act_info[-1]
												a__    =  act_info[0] == 'gated'

												if len(lowerCamelCase_   ) > 1 and act_info[0] != "gated" or len(lowerCamelCase_   ) > 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\''   )

												if feed_forward_proj == "gated-gelu":
																			a__    =  'gelu_new'
					@property
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					int:
												return self.d_model
					@property
					def 				lowerCamelCase__(   self     :int   )      ->					Union[str, Any]:
												return self.num_heads



					@property
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												return self.num_layers







class  snake_case_     (a__   ):
					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def 				lowerCamelCase__(   self     :int   )      ->					Mapping[str, Mapping[int, str]]:
												a__    =  {
												    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
												    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
												}
												if self.use_past:
																			a__    =  'past_encoder_sequence + sequence'
																			a__    =  {0: 'batch'}
																			a__    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
												else:
																			a__    =  {0: 'batch', 1: 'decoder_sequence'}
																			a__    =  {0: 'batch', 1: 'decoder_sequence'}

												if self.use_past:
																			self.fill_with_past_key_values_(lowerCamelCase_    ,direction='inputs'   )

												return common_inputs
					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					int:
												return 13



					@property
					def 				lowerCamelCase__(   self     :str   )      ->					float:
												return 5E-4








# 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
snake_case							: Dict      =   '''
Human: <<task>>

Assistant: '''


snake_case							: Optional[int]      =   '''huggingface-tools/default-prompts'''
snake_case							: Tuple      =   {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Any="run"     ):
							if prompt_or_repo_id is None:
														a__    =  DEFAULT_PROMPTS_REPO

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

							a__    =  cached_file(
							    __lowerCAmelCase      ,					PROMPT_FILES[mode]      ,					repo_type='dataset'      ,					user_agent={'agent': agent_name}     )
							with open(__lowerCAmelCase      ,					'r'      ,					encoding='utf-8'     ) as f:
														return f.read()




