

'''simple docstring'''





import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp

class 						a   (				__magic_name__    ):




				def __get__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]=None      ):
										# See docs.python.org/3/howto/descriptor.html#properties
										if obj is None:
																return self
										if self.fget is None:
																raise AttributeError('''unreadable attribute'''      )
										snake_case       :     Any =    "__cached_" + self.fget.__name__
										snake_case       :     int =    getattr(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )
										if cached is None:
																snake_case       :     Union[str, Any] =    self.fget(UpperCamelCase_      )
																setattr(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_      )
										return cached




def      A (       A_							:					Dict       ):
						snake_case       :     Optional[int] =    val.lower()
						if val in {"y", "yes", "t", "true", "on", "1"}:
												return 1
						if val in {"n", "no", "f", "false", "off", "0"}:
												return 0
						raise ValueError(F"""invalid truth value {val!r}"""       )

def      A (       A_							:					List[str]       ):


						if is_torch_fx_proxy(lowerCamelCase__       ):
												return True
						if is_torch_available():
												import torch

												if isinstance(lowerCamelCase__    ,							torch.Tensor       ):
																		return True
						if is_tf_available():
												import tensorflow as tf

												if isinstance(lowerCamelCase__    ,							tf.Tensor       ):
																		return True

						if is_flax_available():
												import jax.numpy as jnp
												from jax.core import Tracer

												if isinstance(lowerCamelCase__    ,							(jnp.ndarray, Tracer)       ):
																		return True

						return isinstance(lowerCamelCase__    ,							np.ndarray       )

def      A (       A_							:					Union[str, Any]       ):
						return isinstance(lowerCamelCase__    ,							np.ndarray       )

def      A (       A_							:					Any       ):
						return _is_numpy(lowerCamelCase__       )

def      A (       A_							:					Optional[Any]       ):
						import torch

						return isinstance(lowerCamelCase__    ,							torch.Tensor       )

def      A (       A_							:					str       ):
						return False if not is_torch_available() else _is_torch(lowerCamelCase__       )

def      A (       A_							:					int       ):
						import torch

						return isinstance(lowerCamelCase__    ,							torch.device       )

def      A (       A_							:					Any       ):
						return False if not is_torch_available() else _is_torch_device(lowerCamelCase__       )

def      A (       A_							:					Optional[int]       ):
						import torch

						if isinstance(lowerCamelCase__    ,							lowerCamelCase__       ):
												if hasattr(lowerCamelCase__    ,							lowerCamelCase__       ):
																		snake_case       :     Optional[int] =    getattr(lowerCamelCase__    ,							lowerCamelCase__       )
												else:
																		return False
						return isinstance(lowerCamelCase__    ,							torch.dtype       )

def      A (       A_							:					Any       ):
						return False if not is_torch_available() else _is_torch_dtype(lowerCamelCase__       )

def      A (       A_							:					Any       ):
						import tensorflow as tf

						return isinstance(lowerCamelCase__    ,							tf.Tensor       )

def      A (       A_							:					List[str]       ):
						return False if not is_tf_available() else _is_tensorflow(lowerCamelCase__       )

def      A (       A_							:					int       ):
						import tensorflow as tf

						# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
						if hasattr(lowerCamelCase__    ,							'''is_symbolic_tensor'''       ):
												return tf.is_symbolic_tensor(lowerCamelCase__       )
						return type(lowerCamelCase__       ) == tf.Tensor

def      A (       A_							:					Dict       ):
						return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCamelCase__       )

def      A (       A_							:					Optional[int]       ):
						import jax.numpy as jnp  # noqa: F811

						return isinstance(lowerCamelCase__    ,							jnp.ndarray       )

def      A (       A_							:					int       ):
						return False if not is_flax_available() else _is_jax(lowerCamelCase__       )

def      A (       A_							:					int       ):


						if isinstance(lowerCamelCase__    ,							(dict, UserDict)       ):
												return {k: to_py_obj(lowerCamelCase__       ) for k, v in obj.items()}
						elif isinstance(lowerCamelCase__    ,							(list, tuple)       ):
												return [to_py_obj(lowerCamelCase__       ) for o in obj]
						elif is_tf_tensor(lowerCamelCase__       ):
												return obj.numpy().tolist()
						elif is_torch_tensor(lowerCamelCase__       ):
												return obj.detach().cpu().tolist()
						elif is_jax_tensor(lowerCamelCase__       ):
												return np.asarray(lowerCamelCase__       ).tolist()
						elif isinstance(lowerCamelCase__    ,							(np.ndarray, np.number)       ):  # tolist also works on 0d np arrays
												return obj.tolist()
						else:
												return obj

def      A (       A_							:					Union[str, Any]       ):


						if isinstance(lowerCamelCase__    ,							(dict, UserDict)       ):
												return {k: to_numpy(lowerCamelCase__       ) for k, v in obj.items()}
						elif isinstance(lowerCamelCase__    ,							(list, tuple)       ):
												return np.array(lowerCamelCase__       )
						elif is_tf_tensor(lowerCamelCase__       ):
												return obj.numpy()
						elif is_torch_tensor(lowerCamelCase__       ):
												return obj.detach().cpu().numpy()
						elif is_jax_tensor(lowerCamelCase__       ):
												return np.asarray(lowerCamelCase__       )
						else:
												return obj

class 						a   (				__magic_name__    ):




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Optional[int] =    fields(self      )

										# Safety and consistency checks
										if not len(UpperCamelCase_      ):
																raise ValueError(F"""{self.__class__.__name__} has no fields."""      )
										if not all(field.default is None for field in class_fields[1:]      ):
																raise ValueError(F"""{self.__class__.__name__} should not have more than one required field."""      )

										snake_case       :     int =    getattr(self,    class_fields[0].name      )
										snake_case       :     Dict =    all(getattr(self,    field.name      ) is None for field in class_fields[1:]      )

										if other_fields_are_none and not is_tensor(UpperCamelCase_      ):
																if isinstance(UpperCamelCase_,    UpperCamelCase_      ):
																						snake_case       :     int =    first_field.items()
																						snake_case       :     Any =    True
																else:
																						try:
																												snake_case       :     Any =    iter(UpperCamelCase_      )
																												snake_case       :     str =    True
																						except TypeError:
																												snake_case       :     Any =    False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
																if first_field_iterator:
																						for idx, element in enumerate(UpperCamelCase_      ):
																												if (
																												    not isinstance(UpperCamelCase_,    (list, tuple)      )
																												    or not len(UpperCamelCase_      ) == 2
																												    or not isinstance(element[0],    UpperCamelCase_      )
																												):
																																		if idx == 0:
																																								# If we do not have an iterator of key/values, set it as attribute
																																								snake_case       :     List[Any] =    first_field
																																		else:
																																								# If we have a mixed iterator, raise an error
																																								raise ValueError(
																																								    F"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""      )
																																		break
																												setattr(self,    element[0],    element[1]      )
																												if element[1] is not None:
																																		snake_case       :     Tuple =    element[1]
																elif first_field is not None:
																						snake_case       :     Dict =    first_field
										else:
																for field in class_fields:
																						snake_case       :     int =    getattr(self,    field.name      )
																						if v is not None:
																												snake_case       :     Tuple =    v




				def __delitem__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				List[str],    **SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""      )




				def 	__snake_case							(							self	:				Any,    *SCREAMING_SNAKE_CASE_	:				Optional[int],    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""      )




				def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				List[Any],    **SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""      )




				def 	__snake_case							(							self	:				Any,    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""      )




				def __getitem__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										if isinstance(UpperCamelCase_,    UpperCamelCase_      ):
																snake_case       :     List[str] =    dict(self.items()      )
																return inner_dict[k]
										else:
																return self.to_tuple()[k]




				def __setattr__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str      ):
										if name in self.keys() and value is not None:
																# Don't call self.__setitem__ to avoid recursion errors
																super().__setitem__(UpperCamelCase_,    UpperCamelCase_      )
										super().__setattr__(UpperCamelCase_,    UpperCamelCase_      )




				def __setitem__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										# Will raise a KeyException if needed
										super().__setitem__(UpperCamelCase_,    UpperCamelCase_      )
										# Don't call self.__setattr__ to avoid recursion errors
										super().__setattr__(UpperCamelCase_,    UpperCamelCase_      )




				def 	__snake_case							(							self	:				Dict      ):
										return tuple(self[k] for k in self.keys()      )




class 						a   (				__magic_name__				,__magic_name__    ):




				@classmethod
				def 	__snake_case							(							cls	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										raise ValueError(
										    F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()      )}"""      )




class 						a   (				__magic_name__    ):
				_snake_case		    = "longest"
				_snake_case		    = "max_length"
				_snake_case		    = "do_not_pad"




class 						a   (				__magic_name__    ):
				_snake_case		    = "pt"
				_snake_case		    = "tf"
				_snake_case		    = "np"
				_snake_case		    = "jax"




class 						a   :




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     Tuple =    context_managers
										snake_case       :     Optional[int] =    ExitStack()




				def __enter__(							self	:				List[str]      ):
										for context_manager in self.context_managers:
																self.stack.enter_context(UpperCamelCase_      )




				def __exit__(							self	:				Tuple,    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				str      ):
										self.stack.__exit__(*UpperCamelCase_,    **UpperCamelCase_      )




def      A (       A_							:					str       ):
						snake_case       :     List[str] =    infer_framework(lowerCamelCase__       )
						if framework == "tf":
												snake_case       :     List[Any] =    inspect.signature(model_class.call       )  # TensorFlow models
						elif framework == "pt":
												snake_case       :     Dict =    inspect.signature(model_class.forward       )  # PyTorch models
						else:
												snake_case       :     int =    inspect.signature(model_class.__call__       )  # Flax models

						for p in signature.parameters:
												if p == "return_loss" and signature.parameters[p].default is True:
																		return True

						return False

def      A (       A_							:					str       ):
						snake_case       :     List[str] =    model_class.__name__
						snake_case       :     Optional[int] =    infer_framework(lowerCamelCase__       )
						if framework == "tf":
												snake_case       :     int =    inspect.signature(model_class.call       )  # TensorFlow models
						elif framework == "pt":
												snake_case       :     List[Any] =    inspect.signature(model_class.forward       )  # PyTorch models
						else:
												snake_case       :     Any =    inspect.signature(model_class.__call__       )  # Flax models

						if "QuestionAnswering" in model_name:
												return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
						else:
												return [p for p in signature.parameters if "label" in p]

def      A (       A_							:					Tuple    ,							A_							:					List[str] = ""    ,							A_							:					Any = "."       ):


						def _flatten_dict(A_							:					Optional[int]    ,							A_							:					Union[str, Any]=""    ,							A_							:					Any="."       ):
												for k, v in d.items():
																		snake_case       :     Union[str, Any] =    str(lowerCamelCase__       ) + delimiter + str(lowerCamelCase__       ) if parent_key else k
																		if v and isinstance(lowerCamelCase__    ,							lowerCamelCase__       ):
																								yield from flatten_dict(lowerCamelCase__    ,							lowerCamelCase__    ,							delimiter=lowerCamelCase__       ).items()
																		else:
																								yield key, v

						return dict(_flatten_dict(lowerCamelCase__    ,							lowerCamelCase__    ,							lowerCamelCase__       )       )

@contextmanager
def      A (       A_							:					List[str]    ,							A_							:					Tuple = False       ):


						if use_temp_dir:
												with tempfile.TemporaryDirectory() as tmp_dir:
																		yield tmp_dir
						else:
												yield working_dir

def      A (       A_							:					List[Any]    ,							A_							:					Dict=None       ):


						if is_numpy_array(lowerCamelCase__       ):
												return np.transpose(lowerCamelCase__    ,							axes=lowerCamelCase__       )
						elif is_torch_tensor(lowerCamelCase__       ):
												return array.T if axes is None else array.permute(*lowerCamelCase__       )
						elif is_tf_tensor(lowerCamelCase__       ):
												import tensorflow as tf

												return tf.transpose(lowerCamelCase__    ,							perm=lowerCamelCase__       )
						elif is_jax_tensor(lowerCamelCase__       ):
												return jnp.transpose(lowerCamelCase__    ,							axes=lowerCamelCase__       )
						else:
												raise ValueError(F"""Type not supported for transpose: {type(lowerCamelCase__       )}."""       )

def      A (       A_							:					str    ,							A_							:					Union[str, Any]       ):


						if is_numpy_array(lowerCamelCase__       ):
												return np.reshape(lowerCamelCase__    ,							lowerCamelCase__       )
						elif is_torch_tensor(lowerCamelCase__       ):
												return array.reshape(*lowerCamelCase__       )
						elif is_tf_tensor(lowerCamelCase__       ):
												import tensorflow as tf

												return tf.reshape(lowerCamelCase__    ,							lowerCamelCase__       )
						elif is_jax_tensor(lowerCamelCase__       ):
												return jnp.reshape(lowerCamelCase__    ,							lowerCamelCase__       )
						else:
												raise ValueError(F"""Type not supported for reshape: {type(lowerCamelCase__       )}."""       )

def      A (       A_							:					Optional[int]    ,							A_							:					Optional[int]=None       ):


						if is_numpy_array(lowerCamelCase__       ):
												return np.squeeze(lowerCamelCase__    ,							axis=lowerCamelCase__       )
						elif is_torch_tensor(lowerCamelCase__       ):
												return array.squeeze() if axis is None else array.squeeze(dim=lowerCamelCase__       )
						elif is_tf_tensor(lowerCamelCase__       ):
												import tensorflow as tf

												return tf.squeeze(lowerCamelCase__    ,							axis=lowerCamelCase__       )
						elif is_jax_tensor(lowerCamelCase__       ):
												return jnp.squeeze(lowerCamelCase__    ,							axis=lowerCamelCase__       )
						else:
												raise ValueError(F"""Type not supported for squeeze: {type(lowerCamelCase__       )}."""       )

def      A (       A_							:					Dict    ,							A_							:					Tuple       ):


						if is_numpy_array(lowerCamelCase__       ):
												return np.expand_dims(lowerCamelCase__    ,							lowerCamelCase__       )
						elif is_torch_tensor(lowerCamelCase__       ):
												return array.unsqueeze(dim=lowerCamelCase__       )
						elif is_tf_tensor(lowerCamelCase__       ):
												import tensorflow as tf

												return tf.expand_dims(lowerCamelCase__    ,							axis=lowerCamelCase__       )
						elif is_jax_tensor(lowerCamelCase__       ):
												return jnp.expand_dims(lowerCamelCase__    ,							axis=lowerCamelCase__       )
						else:
												raise ValueError(F"""Type not supported for expand_dims: {type(lowerCamelCase__       )}."""       )

def      A (       A_							:					str       ):


						if is_numpy_array(lowerCamelCase__       ):
												return np.size(lowerCamelCase__       )
						elif is_torch_tensor(lowerCamelCase__       ):
												return array.numel()
						elif is_tf_tensor(lowerCamelCase__       ):
												import tensorflow as tf

												return tf.size(lowerCamelCase__       )
						elif is_jax_tensor(lowerCamelCase__       ):
												return array.size
						else:
												raise ValueError(F"""Type not supported for expand_dims: {type(lowerCamelCase__       )}."""       )

def      A (       A_							:					Dict    ,							A_							:					Tuple       ):


						for key, value in auto_map.items():
												if isinstance(lowerCamelCase__    ,							(tuple, list)       ):
																		snake_case       :     List[Any] =    [F"""{repo_id}--{v}""" if (v is not None and "--" not in v) else v for v in value]
												elif value is not None and "--" not in value:
																		snake_case       :     List[str] =    F"""{repo_id}--{value}"""

						return auto_map





def      A (       A_							:					Any       ):


						for base_class in inspect.getmro(lowerCamelCase__       ):
												snake_case       :     Dict =    base_class.__module__
												snake_case       :     Dict =    base_class.__name__
												if module.startswith('''tensorflow'''       ) or module.startswith('''keras'''       ) or name == "TFPreTrainedModel":
																		return "tf"
												elif module.startswith('''torch'''       ) or name == "PreTrainedModel":
																		return "pt"
												elif module.startswith('''flax'''       ) or module.startswith('''jax'''       ) or name == "FlaxPreTrainedModel":
																		return "flax"
						else:
												raise TypeError(F"""Could not infer framework from class {model_class}."""       )



'''simple docstring'''





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase    					=   logging.getLogger()

def      A (       ):
						snake_case       :     Dict =    argparse.ArgumentParser()
						parser.add_argument('''-f'''       )
						snake_case       :     Any =    parser.parse_args()
						return args.f

def      A (       A_							:					str       ):
						snake_case       :     str =    {}
						snake_case       :     Union[str, Any] =    os.path.join(A_    ,							'''all_results.json'''       )
						if os.path.exists(A_       ):
												with open(A_    ,							'''r'''       ) as f:
																		snake_case       :     List[str] =    json.load(A_       )
						else:
												raise ValueError(F"""can't find {path}"""       )
						return results

def      A (       ):
						snake_case       :     Any =    torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


UpperCAmelCase    					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 						a   (				__magic_name__    ):




				@classmethod
				def 	__snake_case							(							cls	:				Tuple      ):
										# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
										snake_case       :     int =    tempfile.mkdtemp()
										snake_case       :     Optional[Any] =    os.path.join(cls.tmpdir,    '''default_config.yml'''      )
										write_basic_config(save_location=cls.configPath      )
										snake_case       :     Tuple =    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




				@classmethod
				def 	__snake_case							(							cls	:				Optional[int]      ):
										shutil.rmtree(cls.tmpdir      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										if is_cuda_and_apex_available():
																testargs.append('''--fp16'''      )

										run_command(self._launch_args + testargs      )
										snake_case       :     int =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''glue_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     int =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										if torch.cuda.device_count() > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										run_command(self._launch_args + testargs      )
										snake_case       :     List[str] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    1_00      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''clm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     int =    F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertLess(result['''perplexity'''],    42      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''mlm_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Dict      ):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										snake_case       :     Any =    7 if get_gpu_count() > 1 else 2

										snake_case       :     Optional[int] =    self.get_auto_remove_tmp_dir()
										snake_case       :     str =    F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.75      )
										self.assertLess(result['''train_loss'''],    0.5      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''ner_no_trainer'''      )      )      )




				@unittest.skip(reason='''Fix me @muellerzr'''      )
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Tuple =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
										self.assertGreaterEqual(result['''eval_f1'''],    28      )
										self.assertGreaterEqual(result['''eval_exact'''],    28      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''qa_no_trainer'''      )      )      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     List[Any] =    F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Union[str, Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.8      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''swag_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Any =    F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     str =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_rouge1'''],    10      )
										self.assertGreaterEqual(result['''eval_rouge2'''],    2      )
										self.assertGreaterEqual(result['''eval_rougeL'''],    7      )
										self.assertGreaterEqual(result['''eval_rougeLsum'''],    7      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''summarization_no_trainer'''      )      )      )




				@slow
				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Any =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[int] =    F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     Optional[int] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_bleu'''],    30      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''epoch_0'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''translation_no_trainer'''      )      )      )




				@slow
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Any =    logging.StreamHandler(sys.stdout      )
										logger.addHandler(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    self.get_auto_remove_tmp_dir()
										snake_case       :     Dict =    F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

										run_command(self._launch_args + testargs      )
										snake_case       :     List[Any] =    get_results(SCREAMING_SNAKE_CASE_      )
										self.assertGreaterEqual(result['''eval_overall_accuracy'''],    0.10      )




				@mock.patch.dict(os.environ,    {'''WANDB_MODE''': '''offline'''}      )
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     str =    self.get_auto_remove_tmp_dir()
										snake_case       :     Optional[Any] =    F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

										if is_cuda_and_apex_available():
																testargs.append('''--fp16'''      )

										run_command(self._launch_args + testargs      )
										snake_case       :     Tuple =    get_results(SCREAMING_SNAKE_CASE_      )
										# The base model scores a 25%
										self.assertGreaterEqual(result['''eval_accuracy'''],    0.6      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''step_1'''      )      )      )
										self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,    '''image_classification_no_trainer'''      )      )      )

