





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowercase							:Any							=	logging.getLogger(__name__)



@dataclass
class  _a  :






							"""simple docstring"""

							snake_case_							=    field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
							snake_case_							=    field(
							    default=lowercase__       ,				metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
							snake_case_							=    field(
							    default=lowercase__       ,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
							snake_case_							=    field(
							    default=lowercase__       ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,				)







@dataclass
class  _a  :






							"""simple docstring"""

							snake_case_							=    field(
							    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()				)}				)
							snake_case_							=    field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}				)
							snake_case_							=    field(
							    default=1_28       ,				metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,				)
							snake_case_							=    field(
							    default=lowercase__       ,				metadata={"help": "Overwrite the cached training and evaluation sets"}				)







def    UpperCAmelCase				(  _lowerCamelCase   :	List[InputFeatures]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       default_data_collator(_lowerCAmelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       batch.pop("pairID"      , _lowerCAmelCase  )
						return batch







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir  )
						    and os.listdir(training_args.output_dir  )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    " --overwrite_output_dir to overcome."  )

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      , datefmt="%m/%d/%Y %H:%M:%S"      , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      , )
						logger.warning(
						    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"      , training_args.local_rank      , training_args.device      , training_args.n_gpu      , bool(training_args.local_rank != -1  )      , training_args.fpaa      , )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank  ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("Training/evaluation parameters %s"      , _lowerCAmelCase  )

						# Set seed
						set_seed(training_args.seed  )

						try:
												SCREAMING_SNAKE_CASE__  :					str =       hans_tasks_num_labels[data_args.task_name]
						except KeyError:
												raise ValueError("Task not found: %s" % (data_args.task_name)  )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path      , num_labels=_lowerCAmelCase      , finetuning_task=data_args.task_name      , cache_dir=model_args.cache_dir      , )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      , cache_dir=model_args.cache_dir      , )
						SCREAMING_SNAKE_CASE__  :					Any =       AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path      , from_tf=bool(".ckpt" in model_args.model_name_or_path  )      , config=_lowerCAmelCase      , cache_dir=model_args.cache_dir      , )

						# Get datasets
						SCREAMING_SNAKE_CASE__  :					Any =       (
						    HansDataset(
						        data_dir=data_args.data_dir      , tokenizer=_lowerCAmelCase      , task=data_args.task_name      , max_seq_length=data_args.max_seq_length      , overwrite_cache=data_args.overwrite_cache      , )
						    if training_args.do_train
						    else None
						)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       (
						    HansDataset(
						        data_dir=data_args.data_dir      , tokenizer=_lowerCAmelCase      , task=data_args.task_name      , max_seq_length=data_args.max_seq_length      , overwrite_cache=data_args.overwrite_cache      , evaluate=_lowerCAmelCase      , )
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE__  :					Tuple =       Trainer(
						    model=_lowerCAmelCase      , args=_lowerCAmelCase      , train_dataset=_lowerCAmelCase      , eval_dataset=_lowerCAmelCase      , data_collator=_lowerCAmelCase      , )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir  )

																		 # Evaluation
						if training_args.do_eval:
												logger.info("*** Evaluate ***"  )

												SCREAMING_SNAKE_CASE__  :					List[Any] =       trainer.predict(_lowerCAmelCase  )
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       output.predictions
												SCREAMING_SNAKE_CASE__  :					str =       np.argmax(_lowerCAmelCase      , axis=1  )

												SCREAMING_SNAKE_CASE__  :					str =       [ex.pairID for ex in eval_dataset]
												SCREAMING_SNAKE_CASE__  :					List[str] =       os.path.join(training_args.output_dir      , "hans_predictions.txt"  )
												SCREAMING_SNAKE_CASE__  :					List[str] =       eval_dataset.get_labels()
												if trainer.is_world_master():
																		with open(_lowerCAmelCase      , "w"  ) as writer:
																								writer.write("pairID,gold_label\n"  )
																								for pid, pred in zip(_lowerCAmelCase      , _lowerCAmelCase  ):
																														writer.write("ex" + str(_lowerCAmelCase  ) + "," + label_list[int(_lowerCAmelCase  )] + "\n"  )

												trainer._log(output.metrics  )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						main()


if __name__ == "__main__":
				main()





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)





