
"""simple docstring"""







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






@dataclass
class      __a      :
       _lowerCamelCase				:				str
       _lowerCamelCase				:				str
       _lowerCamelCase				:				Optional[str]   			=      None
       _lowerCamelCase				:				Optional[str]   			=      None







       def  _snake_case      (       self		):



          '''simple docstring'''

          return json.dumps(dataclasses.asdict(self		)				,indent=2		) + "\n"






@dataclass(frozen=lowerCAmelCase_      )
class      __a      :
       _lowerCamelCase				:				List[int]
       _lowerCamelCase				:				Optional[List[int]]   			=      None
       _lowerCamelCase				:				Optional[List[int]]   			=      None
       _lowerCamelCase				:				Optional[Union[int, float]]   			=      None







       def  _snake_case      (       self		):



          '''simple docstring'''

          return json.dumps(dataclasses.asdict(self		)		) + "\n"






class      __a      :







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          raise NotImplementedError()







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          raise NotImplementedError()







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          raise NotImplementedError()







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          raise NotImplementedError()







       def  _snake_case      (       self		):



          '''simple docstring'''

          raise NotImplementedError()







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          if len(self.get_labels()		) > 1:
             _UpperCAmelCase 						=       self.get_labels()[int(example.label		)]
          return example







       @classmethod
       def  _snake_case      (       cls				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          with open(lowerCamelCase_				,"r"				,encoding="utf-8-sig"		) as f:
             return list(csv.reader(lowerCamelCase_				,delimiter="\t"				,quotechar=lowerCamelCase_		)		)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_="classification"				,lowerCamelCase_=False		):



          '''simple docstring'''

          _UpperCAmelCase 						=       [] if labels is None else labels
          _UpperCAmelCase 						=       [] if examples is None else examples
          _UpperCAmelCase 						=       mode
          _UpperCAmelCase 						=       verbose







       def __len__(       self		):



          '''simple docstring'''

          return len(self.examples		)







       def __getitem__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          if isinstance(lowerCamelCase_				,lowerCamelCase_		):
             return SingleSentenceClassificationProcessor(labels=self.labels				,examples=self.examples[idx]		)
          return self.examples[idx]







       @classmethod
       def  _snake_case      (       cls				,lowerCamelCase_				,lowerCamelCase_=""				,lowerCamelCase_=0				,lowerCamelCase_=1				,lowerCamelCase_=None				,lowerCamelCase_=False				,**lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       cls(**lowerCamelCase_		)
          processor.add_examples_from_csv(
              lowerCamelCase_				,split_name=lowerCamelCase_				,column_label=lowerCamelCase_				,column_text=lowerCamelCase_				,column_id=lowerCamelCase_				,skip_first_row=lowerCamelCase_				,overwrite_labels=lowerCamelCase_				,overwrite_examples=lowerCamelCase_				,)
          return processor







       @classmethod
       def  _snake_case      (       cls				,lowerCamelCase_				,lowerCamelCase_=None				,**lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       cls(**lowerCamelCase_		)
          processor.add_examples(lowerCamelCase_				,labels=lowerCamelCase_		)
          return processor







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=""				,lowerCamelCase_=0				,lowerCamelCase_=1				,lowerCamelCase_=None				,lowerCamelCase_=False				,lowerCamelCase_=False				,lowerCamelCase_=False				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       self._read_tsv(lowerCamelCase_		)
          if skip_first_row:
             _UpperCAmelCase 						=       lines[1:]
          _UpperCAmelCase 						=       []
          _UpperCAmelCase 						=       []
          _UpperCAmelCase 						=       []
          for i, line in enumerate(lowerCamelCase_		):
             texts.append(line[column_text]		)
             labels.append(line[column_label]		)
             if column_id is not None:
                ids.append(line[column_id]		)
             else:
                _UpperCAmelCase 						=       f'''{split_name}-{i}''' if split_name else str(lowerCamelCase_		)
                ids.append(lowerCamelCase_		)

          return self.add_examples(
              lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,overwrite_labels=lowerCamelCase_				,overwrite_examples=lowerCamelCase_		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=None				,lowerCamelCase_=False				,lowerCamelCase_=False		):



          '''simple docstring'''

          if labels is not None and len(lowerCamelCase_		) != len(lowerCamelCase_		):
             raise ValueError(
                 f'''Text and labels have mismatched lengths {len(lowerCamelCase_		)} and {len(lowerCamelCase_		)}'''		)
          if ids is not None and len(lowerCamelCase_		) != len(lowerCamelCase_		):
             raise ValueError(f'''Text and ids have mismatched lengths {len(lowerCamelCase_		)} and {len(lowerCamelCase_		)}'''		)
          if ids is None:
             _UpperCAmelCase 						=       [None] * len(lowerCamelCase_		)
          if labels is None:
             _UpperCAmelCase 						=       [None] * len(lowerCamelCase_		)
          _UpperCAmelCase 						=       []
          _UpperCAmelCase 						=       set()
          for text_or_text_and_label, label, guid in zip(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):
             if isinstance(lowerCamelCase_				,(tuple, list)		) and label is None:
                _UpperCAmelCase						,				_UpperCAmelCase 						=       text_or_text_and_label
             else:
                _UpperCAmelCase 						=       text_or_text_and_label
             added_labels.add(lowerCamelCase_		)
             examples.append(InputExample(guid=lowerCamelCase_				,text_a=lowerCamelCase_				,text_b=lowerCamelCase_				,label=lowerCamelCase_		)		)

          # Update examples
          if overwrite_examples:
             _UpperCAmelCase 						=       examples
          else:
             self.examples.extend(lowerCamelCase_		)

          # Update labels
          if overwrite_labels:
             _UpperCAmelCase 						=       list(lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       list(set(self.labels		).union(lowerCamelCase_		)		)

          return self.examples







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None				,lowerCamelCase_=False				,lowerCamelCase_=0				,lowerCamelCase_=True				,lowerCamelCase_=None				,):



          '''simple docstring'''

          if max_length is None:
             _UpperCAmelCase 						=       tokenizer.max_len

          _UpperCAmelCase 						=       {label: i for i, label in enumerate(self.labels		)}

          _UpperCAmelCase 						=       []
          for ex_index, example in enumerate(self.examples		):
             if ex_index % 1_0000 == 0:
                logger.info(f'''Tokenizing example {ex_index}'''		)

             _UpperCAmelCase 						=       tokenizer.encode(
                 example.text_a				,add_special_tokens=lowerCamelCase_				,max_length=min(lowerCamelCase_				,tokenizer.max_len		)				,)
             all_input_ids.append(lowerCamelCase_		)

          _UpperCAmelCase 						=       max(len(lowerCamelCase_		) for input_ids in all_input_ids		)

          _UpperCAmelCase 						=       []
          for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase_				,self.examples		)		):
             if ex_index % 1_0000 == 0:
                logger.info(f'''Writing example {ex_index}/{len(self.examples		)}'''		)
             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             _UpperCAmelCase 						=       [1 if mask_padding_with_zero else 0] * len(lowerCamelCase_		)

             # Zero-pad up to the sequence length.
             _UpperCAmelCase 						=       batch_length - len(lowerCamelCase_		)
             if pad_on_left:
                _UpperCAmelCase 						=       ([pad_token] * padding_length) + input_ids
                _UpperCAmelCase 						=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
             else:
                _UpperCAmelCase 						=       input_ids + ([pad_token] * padding_length)
                _UpperCAmelCase 						=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

             if len(lowerCamelCase_		) != batch_length:
                raise ValueError(f'''Error with input length {len(lowerCamelCase_		)} vs {batch_length}'''		)
             if len(lowerCamelCase_		) != batch_length:
                raise ValueError(f'''Error with input length {len(lowerCamelCase_		)} vs {batch_length}'''		)

             if self.mode == "classification":
                _UpperCAmelCase 						=       label_map[example.label]
             elif self.mode == "regression":
                _UpperCAmelCase 						=       float(example.label		)
             else:
                raise ValueError(self.mode		)

             if ex_index < 5 and self.verbose:
                logger.info("*** Example ***"		)
                logger.info(f'''guid: {example.guid}'''		)
                logger.info(f'''input_ids: {' '.join([str(lowerCamelCase_		) for x in input_ids]		)}'''		)
                logger.info(f'''attention_mask: {' '.join([str(lowerCamelCase_		) for x in attention_mask]		)}'''		)
                logger.info(f'''label: {example.label} (id = {label})'''		)

             features.append(InputFeatures(input_ids=lowerCamelCase_				,attention_mask=lowerCamelCase_				,label=lowerCamelCase_		)		)

          if return_tensors is None:
             return features
          elif return_tensors == "tf":
             if not is_tf_available():
                raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"		)
             import tensorflow as tf

             def gen():
                for ex in features:
                   yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

             _UpperCAmelCase 						=       tf.data.Dataset.from_generator(
                 lowerCamelCase_				,({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				,({"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)}, tf.TensorShape([]		))				,)
             return dataset
          elif return_tensors == "pt":
             if not is_torch_available():
                raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"		)
             import torch
             from torch.utils.data import TensorDataset

             _UpperCAmelCase 						=       torch.tensor([f.input_ids for f in features]				,dtype=torch.long		)
             _UpperCAmelCase 						=       torch.tensor([f.attention_mask for f in features]				,dtype=torch.long		)
             if self.mode == "classification":
                _UpperCAmelCase 						=       torch.tensor([f.label for f in features]				,dtype=torch.long		)
             elif self.mode == "regression":
                _UpperCAmelCase 						=       torch.tensor([f.label for f in features]				,dtype=torch.float		)

             _UpperCAmelCase 						=       TensorDataset(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		)
             return dataset
          else:
             raise ValueError("return_tensors should be one of 'tf' or 'pt'"		)



"""simple docstring"""







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


SCREAMING_SNAKE_CASE_  					= {}
for task, _ in pipeline_test_mapping.items():
  SCREAMING_SNAKE_CASE_  					= {'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
SCREAMING_SNAKE_CASE_  					= {
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							str:

   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       __UpperCAmelCase		(		a_      :		Optional[Any]     ,	a_      :		int					)  ->							List[Any]:
   # Use the cached results
   if PIPELINE_TEST_MAPPING[task].get(a_     ,	a_					) is not None:
      return PIPELINE_TEST_MAPPING[task][framework]

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

   if framework == "pt":
      _UpperCAmelCase 						=       getattr(a_     ,	"model_mapping"     ,	a_					)
   elif framework == "tf":
      _UpperCAmelCase 						=       getattr(a_     ,	"tf_model_mapping"     ,	a_					)

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

   # cache the results
   _UpperCAmelCase 						=       mapping
   return mapping
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework is None:
      return None
   _UpperCAmelCase 						=       get_mapping_for_task(a_     ,	a_					)
   if mapping is None:
      return None

   _UpperCAmelCase 						=       list({model_class.config_class for model_class in test_class.all_model_classes}					)
   if len(a_					) != 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]     ,	a_					)
   if isinstance(a_     ,	(tuple, list)					):
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: x.__name__					)

   return model_class
def       __UpperCAmelCase		(		a_      :		Tuple					)  ->							Tuple:
   _UpperCAmelCase 						=       [(task, get_model_for_pipeline_test(a_     ,	a_					)) for task in pipeline_test_mapping]
   _UpperCAmelCase 						=       sorted([(task, model) for task, model in mapping if model is not None]     ,	key=lambda a_					: x[0]					)

   return dict(a_					)
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       get_framework(a_					)
   if framework == "pt":
      _UpperCAmelCase 						=       "torch"
   _UpperCAmelCase 						=       "{}"

   _UpperCAmelCase 						=       get_pipeline_model_mapping(a_					)
   if len(a_					) == 0:
      return ""

   _UpperCAmelCase 						=       []
   for task, model_classes in mapping.items():
      if isinstance(a_     ,	(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(a_					) + "}"
   _UpperCAmelCase 						=       F'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

   return text
def       __UpperCAmelCase		(		a_      :		List[Any]					)  ->							Tuple:
   _UpperCAmelCase 						=       {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
   if not issubclass(a_     ,	unittest.TestCase					):
      return False
   return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
def       __UpperCAmelCase		(		a_      :		Optional[Any]					)  ->							Any:
   _UpperCAmelCase 						=       [x for x in get_test_classes(a_					) if is_valid_test_class(a_					)]

   _UpperCAmelCase 						=       None
   for test_class in test_classes:
      # If a test class has defined `pipeline_model_mapping`, let's take it
      if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) 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(a_					) > 0:
      _UpperCAmelCase 						=       sorted(a_     ,	key=lambda a_					: (len(x.__name__					), x.__name__)					)[0]

   return target_test_class
def       __UpperCAmelCase		(		a_      :		Union[str, Any]     ,	a_      :		str     ,	a_      :		List[Any]					)  ->							List[str]:
   _UpperCAmelCase 						=       start_idx
   for idx, line in enumerate(lines[start_idx:]					):
      _UpperCAmelCase 						=       len(a_					) - 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       __UpperCAmelCase		(		a_      :		Tuple     ,	a_      :		Union[str, Any]=False					)  ->							str:
   if getattr(a_     ,	"pipeline_model_mapping"     ,	a_					) is not None:
      if not overwrite:
         return "", -1

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

   # The code defined the class `test_class`
   _UpperCAmelCase						,				_UpperCAmelCase 						=       inspect.getsourcelines(a_					)
   # `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(a_					):
      if line.lstrip().startswith("class "					):
         _UpperCAmelCase 						=       class_lines[idx:]
         class_start_line_no += idx
         break
   _UpperCAmelCase 						=       class_start_line_no + len(a_					) - 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(a_					):
      if line.strip().startswith("all_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("all_generative_model_classes = "					):
         _UpperCAmelCase 						=       len(a_					) - len(line.lstrip()					)
         _UpperCAmelCase 						=       idx
      elif line.strip().startswith("pipeline_model_mapping = "					):
         _UpperCAmelCase 						=       len(a_					) - 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(a_     ,	a_     ,	a_					)

   # 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(a_					)
      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(a_     ,	a_					)
         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(a_     ,	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(a_					):
         # 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(a_					)					)[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(a_					)

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

   return line_to_add
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[Any]=False					)  ->							Dict:
   _UpperCAmelCase 						=       find_test_class(a_					)
   if test_class:
      add_pipeline_model_mapping(a_     ,	overwrite=a_					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_  					= 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`.',
  )
  SCREAMING_SNAKE_CASE_  					= 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.')

  SCREAMING_SNAKE_CASE_  					= []
  if args.test_file:
    SCREAMING_SNAKE_CASE_  					= [args.test_file]
  else:
    SCREAMING_SNAKE_CASE_  					= 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)

