code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  __SCREAMING_SNAKE_CASE					=						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__SCREAMING_SNAKE_CASE					=						[
  			    """Constraint""",
  			    """ConstraintListState""",
  			    """DisjunctiveConstraint""",
  			    """PhrasalConstraint""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """BeamHypotheses""",
  			    """BeamScorer""",
  			    """BeamSearchScorer""",
  			    """ConstrainedBeamSearchScorer""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """EpsilonLogitsWarper""",
  			    """EtaLogitsWarper""",
  			    """ForcedBOSTokenLogitsProcessor""",
  			    """ForcedEOSTokenLogitsProcessor""",
  			    """HammingDiversityLogitsProcessor""",
  			    """InfNanRemoveLogitsProcessor""",
  			    """LogitsProcessor""",
  			    """LogitsProcessorList""",
  			    """LogitsWarper""",
  			    """MinLengthLogitsProcessor""",
  			    """MinNewTokensLengthLogitsProcessor""",
  			    """NoBadWordsLogitsProcessor""",
  			    """NoRepeatNGramLogitsProcessor""",
  			    """PrefixConstrainedLogitsProcessor""",
  			    """RepetitionPenaltyLogitsProcessor""",
  			    """SequenceBiasLogitsProcessor""",
  			    """EncoderRepetitionPenaltyLogitsProcessor""",
  			    """TemperatureLogitsWarper""",
  			    """TopKLogitsWarper""",
  			    """TopPLogitsWarper""",
  			    """TypicalLogitsWarper""",
  			    """EncoderNoRepeatNGramLogitsProcessor""",
  			    """ExponentialDecayLengthPenalty""",
  			    """LogitNormalization""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """MaxNewTokensCriteria""",
  			    """MaxLengthCriteria""",
  			    """MaxTimeCriteria""",
  			    """StoppingCriteria""",
  			    """StoppingCriteriaList""",
  			    """validate_stopping_criteria""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """GenerationMixin""",
  			    """top_k_top_p_filtering""",
  			    """GreedySearchEncoderDecoderOutput""",
  			    """GreedySearchDecoderOnlyOutput""",
  			    """SampleEncoderDecoderOutput""",
  			    """SampleDecoderOnlyOutput""",
  			    """BeamSearchEncoderDecoderOutput""",
  			    """BeamSearchDecoderOnlyOutput""",
  			    """BeamSampleEncoderDecoderOutput""",
  			    """BeamSampleDecoderOnlyOutput""",
  			    """ContrastiveSearchEncoderDecoderOutput""",
  			    """ContrastiveSearchDecoderOnlyOutput""",
  			]
  
  try:
  			if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__SCREAMING_SNAKE_CASE					=						[
  			    """TFForcedBOSTokenLogitsProcessor""",
  			    """TFForcedEOSTokenLogitsProcessor""",
  			    """TFLogitsProcessor""",
  			    """TFLogitsProcessorList""",
  			    """TFLogitsWarper""",
  			    """TFMinLengthLogitsProcessor""",
  			    """TFNoBadWordsLogitsProcessor""",
  			    """TFNoRepeatNGramLogitsProcessor""",
  			    """TFRepetitionPenaltyLogitsProcessor""",
  			    """TFTemperatureLogitsWarper""",
  			    """TFTopKLogitsWarper""",
  			    """TFTopPLogitsWarper""",
  			    """TFForceTokensLogitsProcessor""",
  			    """TFSuppressTokensAtBeginLogitsProcessor""",
  			    """TFSuppressTokensLogitsProcessor""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """TFGenerationMixin""",
  			    """tf_top_k_top_p_filtering""",
  			    """TFGreedySearchDecoderOnlyOutput""",
  			    """TFGreedySearchEncoderDecoderOutput""",
  			    """TFSampleEncoderDecoderOutput""",
  			    """TFSampleDecoderOnlyOutput""",
  			    """TFBeamSearchEncoderDecoderOutput""",
  			    """TFBeamSearchDecoderOnlyOutput""",
  			    """TFBeamSampleEncoderDecoderOutput""",
  			    """TFBeamSampleDecoderOnlyOutput""",
  			    """TFContrastiveSearchEncoderDecoderOutput""",
  			    """TFContrastiveSearchDecoderOnlyOutput""",
  			]
  
  try:
  			if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__SCREAMING_SNAKE_CASE					=						[
  			    """FlaxForcedBOSTokenLogitsProcessor""",
  			    """FlaxForcedEOSTokenLogitsProcessor""",
  			    """FlaxLogitsProcessor""",
  			    """FlaxLogitsProcessorList""",
  			    """FlaxLogitsWarper""",
  			    """FlaxMinLengthLogitsProcessor""",
  			    """FlaxTemperatureLogitsWarper""",
  			    """FlaxTopKLogitsWarper""",
  			    """FlaxTopPLogitsWarper""",
  			]
  			__SCREAMING_SNAKE_CASE					=						[
  			    """FlaxGenerationMixin""",
  			    """FlaxGreedySearchOutput""",
  			    """FlaxSampleOutput""",
  			    """FlaxBeamSearchOutput""",
  			]
  
  if TYPE_CHECKING:
  			from .configuration_utils import GenerationConfig
  			from .streamers import TextIteratorStreamer, TextStreamer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  						from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  						from .logits_process import (
  						    EncoderNoRepeatNGramLogitsProcessor,
  						    EncoderRepetitionPenaltyLogitsProcessor,
  						    EpsilonLogitsWarper,
  						    EtaLogitsWarper,
  						    ExponentialDecayLengthPenalty,
  						    ForcedBOSTokenLogitsProcessor,
  						    ForcedEOSTokenLogitsProcessor,
  						    HammingDiversityLogitsProcessor,
  						    InfNanRemoveLogitsProcessor,
  						    LogitNormalization,
  						    LogitsProcessor,
  						    LogitsProcessorList,
  						    LogitsWarper,
  						    MinLengthLogitsProcessor,
  						    MinNewTokensLengthLogitsProcessor,
  						    NoBadWordsLogitsProcessor,
  						    NoRepeatNGramLogitsProcessor,
  						    PrefixConstrainedLogitsProcessor,
  						    RepetitionPenaltyLogitsProcessor,
  						    SequenceBiasLogitsProcessor,
  						    TemperatureLogitsWarper,
  						    TopKLogitsWarper,
  						    TopPLogitsWarper,
  						    TypicalLogitsWarper,
  						)
  						from .stopping_criteria import (
  						    MaxLengthCriteria,
  						    MaxNewTokensCriteria,
  						    MaxTimeCriteria,
  						    StoppingCriteria,
  						    StoppingCriteriaList,
  						    validate_stopping_criteria,
  						)
  						from .utils import (
  						    BeamSampleDecoderOnlyOutput,
  						    BeamSampleEncoderDecoderOutput,
  						    BeamSearchDecoderOnlyOutput,
  						    BeamSearchEncoderDecoderOutput,
  						    ContrastiveSearchDecoderOnlyOutput,
  						    ContrastiveSearchEncoderDecoderOutput,
  						    GenerationMixin,
  						    GreedySearchDecoderOnlyOutput,
  						    GreedySearchEncoderDecoderOutput,
  						    SampleDecoderOnlyOutput,
  						    SampleEncoderDecoderOutput,
  						    top_k_top_p_filtering,
  						)
  
  			try:
  						if not is_tf_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tf_logits_process import (
  						    TFForcedBOSTokenLogitsProcessor,
  						    TFForcedEOSTokenLogitsProcessor,
  						    TFForceTokensLogitsProcessor,
  						    TFLogitsProcessor,
  						    TFLogitsProcessorList,
  						    TFLogitsWarper,
  						    TFMinLengthLogitsProcessor,
  						    TFNoBadWordsLogitsProcessor,
  						    TFNoRepeatNGramLogitsProcessor,
  						    TFRepetitionPenaltyLogitsProcessor,
  						    TFSuppressTokensAtBeginLogitsProcessor,
  						    TFSuppressTokensLogitsProcessor,
  						    TFTemperatureLogitsWarper,
  						    TFTopKLogitsWarper,
  						    TFTopPLogitsWarper,
  						)
  						from .tf_utils import (
  						    TFBeamSampleDecoderOnlyOutput,
  						    TFBeamSampleEncoderDecoderOutput,
  						    TFBeamSearchDecoderOnlyOutput,
  						    TFBeamSearchEncoderDecoderOutput,
  						    TFContrastiveSearchDecoderOnlyOutput,
  						    TFContrastiveSearchEncoderDecoderOutput,
  						    TFGenerationMixin,
  						    TFGreedySearchDecoderOnlyOutput,
  						    TFGreedySearchEncoderDecoderOutput,
  						    TFSampleDecoderOnlyOutput,
  						    TFSampleEncoderDecoderOutput,
  						    tf_top_k_top_p_filtering,
  						)
  
  			try:
  						if not is_flax_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .flax_logits_process import (
  						    FlaxForcedBOSTokenLogitsProcessor,
  						    FlaxForcedEOSTokenLogitsProcessor,
  						    FlaxLogitsProcessor,
  						    FlaxLogitsProcessorList,
  						    FlaxLogitsWarper,
  						    FlaxMinLengthLogitsProcessor,
  						    FlaxTemperatureLogitsWarper,
  						    FlaxTopKLogitsWarper,
  						    FlaxTopPLogitsWarper,
  						)
  						from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
  			import sys
  
  			__SCREAMING_SNAKE_CASE					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  """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 						__a      (  a,		a					):
  
  
  
  
  
         """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					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return [line for line in lines if not line.lstrip().startswith("#"					) and line != "\n"]
  
  
  
  def 						__a      (  a,		a,		a,		a = None					):
  
  
  
  
  
         """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
  