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#
# 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        a( A			:       List[str]					,	A			:       str			)						->			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\''''			)
					a	,							a   	=  [], False
					for line in lines:
										if not found_start and f'''def {name}''' in line:
															a   	=  True
															good_lines.append(A			)
															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(A			)






def        a( A			:       List[str]			)						->			List[str]:
					"""simple docstring"""





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







def        a( A			:       str					,	A			:       str					,	A			:       bool					,	A			:       str = None			)						->			List[Any]:
					"""simple docstring"""





					with open(A					,	"r"			) as f:
										a   	=  f.readlines()
					with open(os.path.abspath(os.path.join("examples"					,	"nlp_example.py"			)			)					,	"r"			) as f:
										a   	=  f.readlines()
					with open(A					,	"r"			) as f:
										a   	=  f.readlines()
					if secondary_filename is not None:
										with open(A					,	"r"			) as f:
															a   	=  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:
										a   	=  clean_lines(get_function_contents_by_name(A					,	"main"			)			)
										a   	=  clean_lines(get_function_contents_by_name(A					,	"main"			)			)
										a   	=  clean_lines(get_function_contents_by_name(A					,	"main"			)			)
										if secondary_filename is not None:
															a   	=  clean_lines(get_function_contents_by_name(A					,	"main"			)			)
					else:
										a   	=  clean_lines(get_function_contents_by_name(A					,	"training_function"			)			)
										a   	=  clean_lines(get_function_contents_by_name(A					,	"training_function"			)			)
										a   	=  clean_lines(get_function_contents_by_name(A					,	"training_function"			)			)
										if secondary_filename is not None:
															a   	=  clean_lines(
															    get_function_contents_by_name(A					,	"training_function"			)			)

					a   	=  "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
					a   	=  []
					a   	=  []  # We keep track of the idxs just in case it's a repeated statement
					a   	=  iter(A			)
					for i in range(len(A			) - 1			):
										if i not in passed_idxs:
															a   	=  next(A			)
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(A			)
																									passed_idxs.append(A			)
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									a   	=  next(A			)

    # Extract out just the new parts from the full_file_training_func
					a   	=  []
					a   	=  []  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(A			):
										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(A			)
																									passed_idxs.append(A			)

    # Finally, get the overall diff
					a   	=  [line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										a   	=  [line for line in full_file_contents if line not in secondary_file_func]
										a   	=  [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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

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

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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