



'''simple docstring'''






# Copyright 2022 The HuggingFace 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 os
from typing import List




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if name != "training_function" and name != "main":
              raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''						)
       _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					[], False
       for line in lines:
              if not found_start and F'''def {name}''' in line:
                     _UpperCAmelCase    :		int     	=					True
                     good_lines.append(lowerCAmelCase_						)
                     continue
              if found_start:
                     if name == "training_function" and "def main" in line:
                            return good_lines
                     if name == "main" and "if __name__" in line:
                            return good_lines
                     good_lines.append(lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       return [line for line in lines if not line.lstrip().startswith("""#"""						) and line != "\n"]




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None						)->						Optional[Any]:





       '''simple docstring'''


       with open(lowerCAmelCase_			,  """r"""						) as f:
              _UpperCAmelCase    :		List[str]     	=					f.readlines()
       with open(os.path.abspath(os.path.join("""examples"""			,  """nlp_example.py"""						)						)			,  """r"""						) as f:
              _UpperCAmelCase    :		Dict     	=					f.readlines()
       with open(lowerCAmelCase_			,  """r"""						) as f:
              _UpperCAmelCase    :		List[Any]     	=					f.readlines()
       if secondary_filename is not None:
              with open(lowerCAmelCase_			,  """r"""						) as f:
                     _UpperCAmelCase    :		List[Any]     	=					f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
       if parser_only:
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              if secondary_filename is not None:
                     _UpperCAmelCase    :		Optional[Any]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
       else:
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              _UpperCAmelCase    :		Union[str, Any]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              _UpperCAmelCase    :		Any     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              if secondary_filename is not None:
                     _UpperCAmelCase    :		Dict     	=					clean_lines(
                         get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)

       _UpperCAmelCase    :		Union[str, Any]     	=					"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

       # Specific code in our script that differs from the full version, aka what is new
       _UpperCAmelCase    :		str     	=					[]
       _UpperCAmelCase    :		Dict     	=					[]  # We keep track of the idxs just in case it's a repeated statement
       _UpperCAmelCase    :		Tuple     	=					iter(lowerCAmelCase_						)
       for i in range(len(lowerCAmelCase_						) - 1						):
              if i not in passed_idxs:
                     _UpperCAmelCase    :		str     	=					next(lowerCAmelCase_						)
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_feature_code.append(lowerCAmelCase_						)
                                   passed_idxs.append(lowerCAmelCase_						)
                            else:
                                   # Skip over the `config['num_epochs'] = 2` statement
                                   _UpperCAmelCase    :		Optional[Any]     	=					next(lowerCAmelCase_						)

    # Extract out just the new parts from the full_file_training_func
       _UpperCAmelCase    :		Any     	=					[]
       _UpperCAmelCase    :		Optional[int]     	=					[]  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(lowerCAmelCase_						):
              if i not in passed_idxs:
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_full_example_parts.append(lowerCAmelCase_						)
                                   passed_idxs.append(lowerCAmelCase_						)

    # Finally, get the overall diff
       _UpperCAmelCase    :		Optional[Any]     	=					[line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
              _UpperCAmelCase    :		Dict     	=					[line for line in full_file_contents if line not in secondary_file_func]
              _UpperCAmelCase    :		str     	=					[line for line in diff_from_example if line not in diff_from_two]

       return diff_from_example






from collections import OrderedDict
from typing import Mapping

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


A__							=				logging.get_logger(__name__)

A__							=				{
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




