




'''simple docstring'''





import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa




class      UpperCamelCase       (	snake_case__       ):
  a     :Dict          =					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']



  def __init__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      assert Path(hparams.data_dir ).exists()
      _a				:							List[str]							       =      Path(hparams.output_dir )
      self.output_dir.mkdir(exist_ok=__lowerCAmelCase )

      _a				:							List[Any]							       =      self.output_dir.joinpath("""student""" )

      _a				:							Dict							       =      str(__lowerCAmelCase )  # Tell lightning we are training the student
      _a				:							Any							       =      AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher ).eval()
      use_task_specific_params(__lowerCAmelCase		,     hparams.task )  # We copy good generation parameters to student by default
      if hparams.student is not None:
          _a				:							Optional[Any]							       =      AutoModelForSeqaSeqLM.from_pretrained(hparams.student )
          use_task_specific_params(__lowerCAmelCase		,     hparams.task )
          _a       , _a				:							Tuple							       =      None, None
      else:
          _a       , _a       , _a				:							str							       =      create_student_by_copying_alternating_layers(
              __lowerCAmelCase		,     e=hparams.student_encoder_layers		,     d=hparams.student_decoder_layers		,     save_path=__lowerCAmelCase )

      if hparams.length_penalty != -1:
          _a				:							Dict							       =      hparams.length_penalty
      _a				:							Optional[int]							       =      hparams.teacher  # Use teacher's tokenizer
      super().__init__(__lowerCAmelCase		,     model=__lowerCAmelCase		,     config=student.config )
      assert student.config.model_type == teacher.config.model_type, (
          f'''teacher, student model types should be the same, got {student.config.model_type} !='''
          f''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
          _a				:							List[str]							       =      len(student.get_encoder().block )
          _a				:							Any							       =      len(student.get_decoder().block )
          _a				:							Dict							       =      len(teacher.get_encoder().block )
          _a				:							str							       =      len(teacher.get_decoder().block )
      else:
          _a				:							Any							       =      student.config.encoder_layers
          _a				:							Optional[Any]							       =      student.config.decoder_layers
          _a				:							Dict							       =      teacher.config.encoder_layers
          _a				:							Tuple							       =      teacher.config.decoder_layers

      _a				:							List[Any]							       =      not (hparams.student is None or hparams.teacher == hparams.student)
      _a				:							Dict							       =      (not self.different_base_models) and hparams.alpha_hid > 0
      _a				:							str							       =      self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      _a				:							Union[str, Any]							       =      teacher
      freeze_params(self.teacher )

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
          try:
              del self.teacher.model.encoder
          except AttributeError:  # T5
              del self.teacher.encoder

      if e_layer_ids is None:
          _a				:							Dict							       =      list(range(__lowerCAmelCase ) )
      if d_layer_ids is None:
          _a				:							int							       =      list(range(__lowerCAmelCase ) )

      _a       , _a				:							Dict							       =      e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
          if hparams.supervise_forward:
              _a				:							Optional[Any]							       =      get_layers_to_supervise(
                  n_student=len(self.e_layer_ids )		,     n_teacher=__lowerCAmelCase )
              _a				:							List[Any]							       =      get_layers_to_supervise(
                  n_student=len(self.d_layer_ids )		,     n_teacher=__lowerCAmelCase )
          else:  # student layer should emulate hidden states of the teacher layer it was copied from
              _a				:							Dict							       =      self.e_layer_ids
              _a				:							List[Any]							       =      self.d_layer_ids
      else:
          _a				:							Union[str, Any]							       =      None
          _a				:							int							       =      None

      _a				:							int							       =      nn.KLDivLoss(reduction="""batchmean""" )
      _a				:							int							       =      2.0
      _a				:							List[str]							       =      hparams.alpha_mlm
      _a				:							int							       =      hparams.alpha_ce
      _a				:							Dict							       =      hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      mask[:, :, None].expand_as(__lowerCAmelCase )
      _a				:							str							       =      s_logits.size(-1 )
      _a				:							Union[str, Any]							       =      torch.masked_select(__lowerCAmelCase		,     __lowerCAmelCase )  # (bs * seq_length * voc_size) modulo the 1s in mask
      _a				:							Union[str, Any]							       =      torch.masked_select(__lowerCAmelCase		,     __lowerCAmelCase )  # (bs * seq_length * voc_size) modulo the 1s in mask
      _a				:							List[Any]							       =      s_logits_slct.view(-1		,     __lowerCAmelCase )  # (bs * seq_length, voc_size) modulo the 1s in mask
      _a				:							Any							       =      t_logits_slct.view(-1		,     __lowerCAmelCase )  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      _a				:							List[str]							       =      (
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature		,     dim=-1 )		,     nn.functional.softmax(t_logits_slct / self.temperature		,     dim=-1 )		,     )
          * (self.temperature) ** 2
      )
      return loss_ce



  @staticmethod
  def     a_							(			__lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      SummarizationModule.add_model_specific_args(__lowerCAmelCase		,     __lowerCAmelCase )
      add_distill_args(__lowerCAmelCase )
      return parser



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      self.tokenizer.pad_token_id
      _a       , _a       , _a				:							Any							       =      batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
      if isinstance(self.model		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      self.model._shift_right(__lowerCAmelCase )
      else:
          _a				:							Dict							       =      shift_tokens_right(__lowerCAmelCase		,     __lowerCAmelCase )

      # noinspection PyCallingNonCallable
      _a				:							Any							       =      self(
          __lowerCAmelCase		,     attention_mask=__lowerCAmelCase		,     decoder_input_ids=__lowerCAmelCase		,     output_hidden_states=self.do_calc_hidden_loss		,     output_attentions=__lowerCAmelCase		,     use_cache=__lowerCAmelCase		,     )
      _a				:							List[Any]							       =      student_outputs["""logits"""]

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
          # Same behavior as modeling_bart.py, besides ignoring pad_token_id
          _a				:							Optional[int]							       =      nn.CrossEntropyLoss(ignore_index=__lowerCAmelCase )
          _a				:							Optional[int]							       =      loss_fct(lm_logits.view(-1		,     lm_logits.shape[-1] )		,     labels.view(-1 ) )
      else:
          _a				:							str							       =      nn.functional.log_softmax(__lowerCAmelCase		,     dim=-1 )
          _a       , _a				:							int							       =      label_smoothed_nll_loss(
              __lowerCAmelCase		,     __lowerCAmelCase		,     self.hparams.label_smoothing		,     ignore_index=__lowerCAmelCase )

      def zero_tensor():
          return torch.tensor(0.0 ).type_as(__lowerCAmelCase )

      _a				:							Optional[int]							       =      student_outputs[
          """encoder_last_hidden_state"""
      ]  # use this unless self.different_base_models
      _a       , _a				:							str							       =      zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
          _a				:							int							       =      self.teacher.get_encoder()(
              __lowerCAmelCase		,     attention_mask=__lowerCAmelCase		,     output_hidden_states=self.do_calc_hidden_loss		,     )
          if self.different_base_models:
              _a				:							Union[str, Any]							       =      all_teacher_encoder_outputs["""last_hidden_state"""]
          elif self.do_calc_hidden_loss:
              _a				:							Optional[Any]							       =      self.calc_hidden_loss(
                  __lowerCAmelCase		,     student_outputs["""encoder_hidden_states"""]		,     all_teacher_encoder_outputs["""hidden_states"""]		,     self.e_matches		,     normalize_hidden=self.hparams.normalize_hidden		,     )

      _a				:							Any							       =      self.teacher(
          __lowerCAmelCase		,     attention_mask=__lowerCAmelCase		,     encoder_outputs=(teacher_enc_outputs,)		,     decoder_input_ids=__lowerCAmelCase		,     output_hidden_states=self.do_calc_hidden_loss		,     use_cache=__lowerCAmelCase		,     )
      _a				:							Optional[int]							       =      decoder_input_ids.ne(__lowerCAmelCase )
      _a				:							int							       =      self.calc_ce_loss(__lowerCAmelCase		,     __lowerCAmelCase		,     teacher_outputs["""logits"""] )
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
          _a				:							int							       =      self.calc_hidden_loss(
              __lowerCAmelCase		,     student_outputs["""decoder_hidden_states"""]		,     teacher_outputs["""decoder_hidden_states"""]		,     self.d_matches		,     normalize_hidden=self.hparams.normalize_hidden		,     )

      _a				:							Tuple							       =      (
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec



  @staticmethod
  def     a_							(			__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Any							       =      """expected list or tuple for hidden_states, got tensor of shape: """
      assert not isinstance(__lowerCAmelCase		,     torch.Tensor ), f'''{msg}{hidden_states.shape}'''
      assert not isinstance(__lowerCAmelCase		,     torch.Tensor ), f'''{msg}{hidden_states_T.shape}'''
      _a				:							str							       =      attention_mask.to(hidden_states[0] )
      _a				:							Union[str, Any]							       =      mask.sum() * hidden_states[0].size(-1 )
      _a				:							int							       =      torch.stack([hidden_states[i] for i in range(len(__lowerCAmelCase ) )] )
      _a				:							Tuple							       =      torch.stack([hidden_states_T[j] for j in matches] )
      assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
          _a				:							List[str]							       =      nn.functional.layer_norm(__lowerCAmelCase		,     student_states.shape[1:] )
          _a				:							Optional[int]							       =      nn.functional.layer_norm(__lowerCAmelCase		,     teacher_states.shape[1:] )
      _a				:							List[Any]							       =      nn.functional.mse_loss(__lowerCAmelCase		,     __lowerCAmelCase		,     reduction="""none""" )
      _a				:							Optional[Any]							       =      (mse * mask.unsqueeze(0 ).unsqueeze(-1 )).sum() / valid_count
      return masked_mse






def UpperCAmelCase    (       UpperCAmelCase_						):
    # NOTE: if --student argument was specified and the teacher and student base models
    # are different, the models still have to have the same tokenizer, specified by
    # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
    # from bart to t5. This s because if the tokenizers are different, the output space
    # for the two models is also different and their logits are not comparable.
    parser.add_argument("""--teacher"""   ,	type=UpperCAmelCase_						)
    parser.add_argument("""--alpha_ce"""   ,	default=0.8   ,	type=UpperCAmelCase_						)
    parser.add_argument("""--alpha_mlm"""   ,	default=0.2   ,	type=UpperCAmelCase_						)
    parser.add_argument("""--alpha_hid"""   ,	default=0.0   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_						)
    parser.add_argument("""--student"""   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_						)
    parser.add_argument("""--student_decoder_layers"""   ,	default=12   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_						)
    parser.add_argument("""--student_encoder_layers"""   ,	default=12   ,	type=UpperCAmelCase_   ,	required=UpperCAmelCase_						)
    parser.add_argument("""--no_teacher"""   ,	action="""store_true"""   ,	default=UpperCAmelCase_						)
    parser.add_argument("""--length_penalty"""   ,	type=UpperCAmelCase_   ,	default=-1						)
    parser.add_argument("""--supervise_forward"""   ,	action="""store_true"""   ,	default=UpperCAmelCase_						)
    parser.add_argument("""--normalize_hidden"""   ,	action="""store_true"""   ,	default=UpperCAmelCase_						)




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					'translation'
  a     :int          =					['bleu']
  a     :str          =					'bleu'



  def __init__(			self		,     __lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      super().__init__(__lowerCAmelCase		,     **__lowerCAmelCase )
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      _a				:							Dict							       =      hparams.src_lang
      _a				:							Optional[int]							       =      hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      self.tokenizer.lang_code_to_id[hparams.tgt_lang]



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      return calculate_bleu(__lowerCAmelCase		,     __lowerCAmelCase )



  @staticmethod
  def     a_							(			__lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      TranslationModule.add_model_specific_args(__lowerCAmelCase		,     __lowerCAmelCase )
      add_distill_args(__lowerCAmelCase )
      return parser






def UpperCAmelCase    (       UpperCAmelCase_						):
    if args.no_teacher:
        _a				:							str							       =      TranslationModule if """translation""" in args.task else SummarizationModule
    else:  # DISTILL WITH TEACHER
        _a				:							int							       =      TranslationDistiller if """translation""" in args.task else SummarizationDistiller
    _a				:							str							       =      module_cls.__name__
    print(F'''using module {args.setup_cls}'''						)
    _a				:							int							       =      module_cls(UpperCAmelCase_						)
    return model





def UpperCAmelCase    (       UpperCAmelCase_						):
    Path(args.output_dir						).mkdir(exist_ok=UpperCAmelCase_						)
    check_output_dir(UpperCAmelCase_   ,	expected_items=3						)

    _a				:							Union[str, Any]							       =      create_module(UpperCAmelCase_						)
    return ft_main(UpperCAmelCase_   ,	model=UpperCAmelCase_						)


if __name__ == "__main__":
  a		:	Dict       =      argparse.ArgumentParser()
  a		:	Optional[int]       =      pl.Trainer.add_argparse_args(parser)
  a		:	Dict       =      SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  a		:	Optional[Any]       =      parser.parse_args()

  distill_main(args)








import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


_lowerCAmelCase    : int		= {}
for task, _ in pipeline_test_mapping.items():
  _lowerCAmelCase    : List[str]		= {"pt": None, "tf": None}


# DO **NOT** add item to this set (unless the reason is approved)
_lowerCAmelCase    : Optional[int]		= {
    "tests/models/esm/test_modeling_esmfold.py",  # The pipeline test mapping is added to `test_modeling_esm.py`
}



def     lowerCAmelCase		(		_lowerCAmelCase				: Union[str, Any]						):




 """simple docstring"""







 if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "pt"
 elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "tf"
 elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  return "flax"
 else:
  return None



def     lowerCAmelCase		(		_lowerCAmelCase				: str    ,				_lowerCAmelCase				: Optional[Any]						):




 """simple docstring"""







 if PIPELINE_TEST_MAPPING[task].get(_lowerCAmelCase    ,				_lowerCAmelCase						) is not None:
  return PIPELINE_TEST_MAPPING[task][framework]

 UpperCAmelCase__							=    pipeline_test_mapping[task]["test"]
 UpperCAmelCase__							=    None

 if framework == "pt":
  UpperCAmelCase__							=    getattr(_lowerCAmelCase    ,				"model_mapping"    ,				_lowerCAmelCase						)
 elif framework == "tf":
  UpperCAmelCase__							=    getattr(_lowerCAmelCase    ,				"tf_model_mapping"    ,				_lowerCAmelCase						)

 if mapping is not None:
  UpperCAmelCase__							=    dict(mapping.items()						)

 # cache the results
 UpperCAmelCase__							=    mapping
 return mapping



def     lowerCAmelCase		(		_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: List[str]						):




 """simple docstring"""







 UpperCAmelCase__							=    get_framework(_lowerCAmelCase						)
 if framework is None:
  return None
 UpperCAmelCase__							=    get_mapping_for_task(_lowerCAmelCase    ,				_lowerCAmelCase						)
 if mapping is None:
  return None

 UpperCAmelCase__							=    list({model_class.config_class for model_class in test_class.all_model_classes}						)
 if len(_lowerCAmelCase						) != 1:
  raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`."						)

 # This could be a list/tuple of model classes, but it's rare.
 UpperCAmelCase__							=    mapping.get(config_classes[0]    ,				_lowerCAmelCase						)
 if isinstance(_lowerCAmelCase    ,				(tuple, list)						):
  UpperCAmelCase__							=    sorted(_lowerCAmelCase    ,				key=lambda _lowerCAmelCase						: x.__name__						)

 return model_class



def     lowerCAmelCase		(		_lowerCAmelCase				: str						):




 """simple docstring"""







 UpperCAmelCase__							=    [(task, get_model_for_pipeline_test(_lowerCAmelCase    ,				_lowerCAmelCase						)) for task in pipeline_test_mapping]
 UpperCAmelCase__							=    sorted([(task, model) for task, model in mapping if model is not None]    ,				key=lambda _lowerCAmelCase						: x[0]						)

 return dict(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[Any]						):




 """simple docstring"""







 UpperCAmelCase__							=    get_framework(_lowerCAmelCase						)
 if framework == "pt":
  UpperCAmelCase__							=    "torch"
 UpperCAmelCase__							=    "{}"

 UpperCAmelCase__							=    get_pipeline_model_mapping(_lowerCAmelCase						)
 if len(_lowerCAmelCase						) == 0:
  return ""

 UpperCAmelCase__							=    []
 for task, model_classes in mapping.items():
  if isinstance(_lowerCAmelCase    ,				(tuple, list)						):
   # A list/tuple of model classes
   UpperCAmelCase__							=    "(" + ", ".join([x.__name__ for x in model_classes]						) + ")"
  else:
   # A single model class
   UpperCAmelCase__							=    model_classes.__name__
  texts.append(F'''"{task}": {value}'''						)
 UpperCAmelCase__							=    "{" + ", ".join(_lowerCAmelCase						) + "}"
 UpperCAmelCase__							=    F'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

 return text



def     lowerCAmelCase		(		_lowerCAmelCase				: Any						):




 """simple docstring"""







 UpperCAmelCase__							=    {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
 if not issubclass(_lowerCAmelCase    ,				unittest.TestCase						):
  return False
 return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]						)						) > 0



def     lowerCAmelCase		(		_lowerCAmelCase				: Union[str, Any]						):




 """simple docstring"""







 UpperCAmelCase__							=    [x for x in get_test_classes(_lowerCAmelCase						) if is_valid_test_class(_lowerCAmelCase						)]

 UpperCAmelCase__							=    None
 for test_class in test_classes:
  # If a test class has defined `pipeline_model_mapping`, let's take it
  if getattr(_lowerCAmelCase    ,				"pipeline_model_mapping"    ,				_lowerCAmelCase						) is not None:
   UpperCAmelCase__							=    test_class
   break
    # Take the test class with the shortest name (just a heuristic)
 if target_test_class is None and len(_lowerCAmelCase						) > 0:
  UpperCAmelCase__							=    sorted(_lowerCAmelCase    ,				key=lambda _lowerCAmelCase						: (len(x.__name__						), x.__name__)						)[0]

 return target_test_class



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Optional[int]    ,				_lowerCAmelCase				: str						):




 """simple docstring"""







 UpperCAmelCase__							=    start_idx
 for idx, line in enumerate(lines[start_idx:]						):
  UpperCAmelCase__							=    len(_lowerCAmelCase						) - len(line.lstrip()						)
  if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
   UpperCAmelCase__							=    start_idx + idx
  elif idx > 0 and indent <= indent_level:
   # Outside the definition block of `pipeline_model_mapping`
   break

 return end_idx



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: str=False						):




 """simple docstring"""







 if getattr(_lowerCAmelCase    ,				"pipeline_model_mapping"    ,				_lowerCAmelCase						) is not None:
  if not overwrite:
   return "", -1

 UpperCAmelCase__							=    get_pipeline_model_mapping_string(_lowerCAmelCase						)
 if len(_lowerCAmelCase						) == 0:
  return "", -1
 UpperCAmelCase__							=    line_to_add + "\n"

 # The code defined the class `test_class`
 UpperCAmelCase__			,			UpperCAmelCase__							=    inspect.getsourcelines(_lowerCAmelCase						)
 # `inspect` gives the code for an object, including decorator(s) if any.
 # We (only) need the exact line of the class definition.
 for idx, line in enumerate(_lowerCAmelCase						):
  if line.lstrip().startswith("class "						):
   UpperCAmelCase__							=    class_lines[idx:]
   class_start_line_no += idx
   break
 UpperCAmelCase__							=    class_start_line_no + len(_lowerCAmelCase						) - 1

 # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
 # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
 # block that appears in a `test_class`.
 UpperCAmelCase__							=    None
 # The indent level of the line at `class_lines[start_idx]` (if defined)
 UpperCAmelCase__							=    0
 # To record if `pipeline_model_mapping` is found in `test_class`.
 UpperCAmelCase__							=    None
 for idx, line in enumerate(_lowerCAmelCase						):
  if line.strip().startswith("all_model_classes = "						):
   UpperCAmelCase__							=    len(_lowerCAmelCase						) - len(line.lstrip()						)
   UpperCAmelCase__							=    idx
  elif line.strip().startswith("all_generative_model_classes = "						):
   UpperCAmelCase__							=    len(_lowerCAmelCase						) - len(line.lstrip()						)
   UpperCAmelCase__							=    idx
  elif line.strip().startswith("pipeline_model_mapping = "						):
   UpperCAmelCase__							=    len(_lowerCAmelCase						) - len(line.lstrip()						)
   UpperCAmelCase__							=    idx
   UpperCAmelCase__							=    line
   break

 if start_idx is None:
  return "", -1
 # Find the ending index (inclusive) of the above found block.
 UpperCAmelCase__							=    find_block_ending(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)

 # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
 # `is_timm_available()` instead of `is_torch_available()`.
 # Keep leading and trailing whitespaces
 UpperCAmelCase__							=    re.compile(R"\s(is_\S+?_available\(\))\s"						)
 for line in class_lines[start_idx : end_idx + 1]:
  UpperCAmelCase__							=    r.search(_lowerCAmelCase						)
  if backend_condition is not None:
   # replace the leading and trailing whitespaces to the space character " ".
   UpperCAmelCase__							=    " " + backend_condition[0][1:-1] + " "
   UpperCAmelCase__							=    r.sub(_lowerCAmelCase    ,				_lowerCAmelCase						)
   break

 if def_line is None:
  # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
  # `all_model_classes` or `all_generative_model_classes`.
  UpperCAmelCase__							=    end_idx
 else:
  # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
  UpperCAmelCase__							=    start_idx - 1
  # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
  for idx in range(_lowerCAmelCase    ,				end_idx + 1						):
   # These lines are going to be removed before writing to the test file.
   UpperCAmelCase__							=    None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
 UpperCAmelCase__							=    [x.__name__ for x in test_class.__bases__]
 if "PipelineTesterMixin" not in parent_classes:
  # Put `PipelineTesterMixin` just before `unittest.TestCase`
  UpperCAmelCase__							=    [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"]
  if "TestCase" in parent_classes:
   # Here we **assume** the original string is always with `unittest.TestCase`.
   _parent_classes.append("unittest.TestCase"						)
  UpperCAmelCase__							=    ", ".join(_parent_classes						)
  for idx, line in enumerate(_lowerCAmelCase						):
   # Find the ending of the declaration of `test_class`
   if line.strip().endswith("):"						):
    # mark the lines of the declaration of `test_class` to be removed
    for _idx in range(idx + 1						):
     UpperCAmelCase__							=    None  # noqa
    break
        # Add the new, one-line, class declaration for `test_class`
  UpperCAmelCase__							=    F'''class {test_class.__name__}({parent_classes}):\n'''

 # Add indentation
 UpperCAmelCase__							=    " " * indent_level + line_to_add
 # Insert `pipeline_model_mapping` to `class_lines`.
 # (The line at `target_idx` should be kept by definition!)
 UpperCAmelCase__							=    class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
 # Remove the lines that are marked to be removed
 UpperCAmelCase__							=    [x for x in class_lines if x is not None]

 # Move from test class to module (in order to write to the test file)
 UpperCAmelCase__							=    inspect.getsourcelines(inspect.getmodule(_lowerCAmelCase						)						)[0]
 # Be careful with the 1-off between line numbers and array indices
 UpperCAmelCase__							=    module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
 UpperCAmelCase__							=    "".join(_lowerCAmelCase						)

 UpperCAmelCase__							=    inspect.getsourcefile(_lowerCAmelCase						)
 with open(_lowerCAmelCase    ,				"w"    ,				encoding="UTF-8"    ,				newline="\n"						) as fp:
  fp.write(_lowerCAmelCase						)

 return line_to_add



def     lowerCAmelCase		(		_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: List[Any]=False						):




 """simple docstring"""







 UpperCAmelCase__							=    find_test_class(_lowerCAmelCase						)
 if test_class:
  add_pipeline_model_mapping(_lowerCAmelCase    ,				overwrite=_lowerCAmelCase						)


if __name__ == "__main__":
  _lowerCAmelCase    : Tuple		= argparse.ArgumentParser()
  parser.add_argument(
      "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory."
  )
  parser.add_argument(
      "--all",
      action="store_true",
      help="If to check and modify all test files.",
  )
  parser.add_argument(
      "--overwrite",
      action="store_true",
      help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.",
  )
  _lowerCAmelCase    : Optional[Any]		= parser.parse_args()

  if not args.all and not args.test_file:
    raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.")
  elif args.all and args.test_file:
    raise ValueError("Only one of `--test_file` and `--all` could be specified.")

  _lowerCAmelCase    : List[str]		= []
  if args.test_file:
    _lowerCAmelCase    : Any		= [args.test_file]
  else:
    _lowerCAmelCase    : Any		= os.path.join("tests", "models", "**", "test_modeling_*.py")
    for test_file in glob.glob(pattern):
      # `Flax` is not concerned at this moment
      if not test_file.startswith("test_modeling_flax_"):
        test_files.append(test_file)

  for test_file in test_files:
    if test_file in TEST_FILE_TO_IGNORE:
      print(F'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
      continue
    add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)





