"""simple docstring"""






import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowerCAmelCase__						:						Tuple		  =			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
						from run_translation import main  # noqa


set_seed(42)
lowerCAmelCase__						:						List[str]		  =			'sshleifer/student_marian_en_ro_6_1'
lowerCAmelCase__						:						Optional[int]		  =			'sshleifer/tiny-mbart'
@require_torch
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Tuple=False					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			Optional[int]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			int=True					,):
									UpperCAmelCase__    =							self.run_trainer(
									    eval_steps=1					,max_len=12					,model_name=lowerCamelCase__					,num_train_epochs=1					,distributed=lowerCamelCase__					,extra_args_str=lowerCamelCase__					,predict_with_generate=lowerCamelCase__					,do_train=lowerCamelCase__					,do_eval=lowerCamelCase__					,do_predict=lowerCamelCase__					,)
									UpperCAmelCase__    =							TrainerState.load_from_json(os.path.join(lowerCamelCase__					,'trainer_state.json'    )    ).log_history

									if not do_eval:
													return

									UpperCAmelCase__    =							[log for log in logs if 'eval_loss' in log.keys()]

									UpperCAmelCase__    =							eval_metrics[0]
									if predict_with_generate:
													assert "eval_bleu" in first_step_stats

													UpperCAmelCase__    =							eval_metrics[-1]
													assert isinstance(last_step_stats['eval_bleu']					,lowerCamelCase__    )
													assert not math.isnan(float(last_step_stats['eval_loss']    )    ), "eval_loss must not be `nan`"







					@require_torch_non_multi_gpu
					def 	__lowerCAmelCase		(       self     :			Any    ):
									self.run_seqaseq_quick()







					@require_torch_multi_gpu
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__    )







					@require_torch_multi_gpu
					def 	__lowerCAmelCase		(       self     :			int    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__    )







					@unittest.skip('Requires an update of the env running those tests'    )
					@require_torch_multi_gpu
					@require_fairscale
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--sharded_ddp simple'    )







					@unittest.skip('Requires an update of the env running those tests'    )
					@require_torch_multi_gpu
					@require_fairscale
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--sharded_ddp simple --fp16'    )







					@unittest.skip('Requires an update of the env running those tests'    )
					@require_torch_multi_gpu
					@require_fairscale
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--sharded_ddp zero_dp_2'					,predict_with_generate=lowerCamelCase__    )







					@unittest.skip('Requires an update of the env running those tests'    )
					@require_torch_multi_gpu
					@require_fairscale
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									self.run_seqaseq_quick(
									    distributed=lowerCamelCase__					,extra_args_str='--sharded_ddp zero_dp_2 --fp16'					,predict_with_generate=lowerCamelCase__    )







					@require_apex
					@require_torch_gpu
					def 	__lowerCAmelCase		(       self     :			int    ):
									# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
									# program and it breaks other tests that run from the same pytest worker, therefore until this is
									# sorted out it must be run only in an external program, that is distributed=True in this
									# test and only under one or more gpus - if we want cpu will need to make a special test
									#
									# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
									# 2nd main() call it botches the future eval.
									#
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--fp16 --fp16_backend=apex'    )
									# test 2nd time - was getting eval_loss': nan'
									# to reproduce the problem set distributed=False
									self.run_seqaseq_quick(distributed=lowerCamelCase__					,extra_args_str='--fp16 --fp16_backend=apex'    )







					@parameterized.expand(['base', 'low', 'high', 'mixed']    )
					@require_torch_multi_gpu
					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Any    ):
									# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
									UpperCAmelCase__    =							{
									    # test with the default log_level - should be info and thus log info once
									    'base': {'extra_args_str': '', 'n_matches': 1},
									    # test with low log_level and log_level_replica - should be noisy on all processes
									    # now the info string should appear twice on 2 processes
									    'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
									    # test with high log_level and low log_level_replica
									    # now the info string should appear once only on the replica
									    'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
									    # test with high log_level and log_level_replica - should be quiet on all processes
									    'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
									}

									UpperCAmelCase__    =							experiments[experiment_id]
									UpperCAmelCase__    =							{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
									UpperCAmelCase__    =							'Running training'
									with CaptureStderr() as cl:
													self.run_seqaseq_quick(**lowerCamelCase__					,extra_args_str=data['extra_args_str']    )
									UpperCAmelCase__    =							len(re.findall(lowerCamelCase__					,cl.err    )    )
									self.assertEqual(lowerCamelCase__					,data['n_matches']    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.run_trainer(
									    eval_steps=2					,max_len=128					,model_name=lowerCamelCase__					,learning_rate=3e-4					,num_train_epochs=10					,distributed=lowerCamelCase__					,)

									# Check metrics
									UpperCAmelCase__    =							TrainerState.load_from_json(os.path.join(lowerCamelCase__					,'trainer_state.json'    )    ).log_history
									UpperCAmelCase__    =							[log for log in logs if 'eval_loss' in log.keys()]
									UpperCAmelCase__    =							eval_metrics[0]
									UpperCAmelCase__    =							eval_metrics[-1]

									assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
									assert isinstance(last_step_stats['eval_bleu']					,lowerCamelCase__    )

									# test if do_predict saves generations and metrics
									UpperCAmelCase__    =							os.listdir(lowerCamelCase__    )
									UpperCAmelCase__    =							{os.path.basename(lowerCamelCase__    ) for p in contents}
									assert "generated_predictions.txt" in contents
									assert "predict_results.json" in contents







					@slow
					@require_bitsandbytes
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									from transformers.training_args import OptimizerNames

									def train_and_return_metrics(lowerCamelCase__     :			str    ) -> Tuple[int, float]:
													UpperCAmelCase__    =							'--skip_memory_metrics 0'

													UpperCAmelCase__    =							self.run_trainer(
													    max_len=128					,model_name=lowerCamelCase__					,learning_rate=3e-4					,num_train_epochs=1					,optim=lowerCamelCase__					,distributed=lowerCamelCase__					,extra_args_str=lowerCamelCase__					,do_eval=lowerCamelCase__					,do_predict=lowerCamelCase__					,n_gpus_to_use=1					,)

													# Check metrics
													UpperCAmelCase__    =							TrainerState.load_from_json(Path(lowerCamelCase__					,'trainer_state.json'    )    ).log_history
													UpperCAmelCase__    =							int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20    )
													UpperCAmelCase__    =							int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20    )

													UpperCAmelCase__    =							logs[0]['train_loss']
													return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

									UpperCAmelCase__    =							gpu_alloc_mem_orig - gpu_alloc_mem_bnb

									UpperCAmelCase__    =							gpu_peak_mem_orig + gpu_alloc_mem_orig
									UpperCAmelCase__    =							gpu_peak_mem_bnb + gpu_alloc_mem_bnb
									UpperCAmelCase__    =							gpu_total_mem_orig - gpu_total_mem_bnb

									# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
									# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
									# in 2 bytes and the diff in optim memory usage is derived as so:
									#
									# - normal 25*8=~200MB (8 bytes per param)
									# - bnb    25*2= ~50MB (2 bytes per param)
									#
									# Thus we should expect ~150MB total memory saved.
									#
									# Peak memory should be the same - the total should be different by about that same margin
									#
									# After leaving a small margin to accommodate for differences between gpus let's check
									# that we have at least 120MB in savings
									UpperCAmelCase__    =							120

									# uncomment the following if this test starts failing - requires py38 for a new print feature
									# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
									# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
									# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
									# print(f"{gpu_alloc_mem_diff=}MB")
									# print(f"{gpu_peak_mem_diff=}MB")
									# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
									# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

									self.assertGreater(
									    lowerCamelCase__					,lowerCamelCase__					,'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
									    f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
									    f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''					,)

									self.assertGreater(
									    lowerCamelCase__					,lowerCamelCase__					,'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
									    f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
									    f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''					,)

									self.assertEqual(
									    lowerCamelCase__					,lowerCamelCase__					,f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			float = 3e-3					,lowerCamelCase__     :			str = "adafactor"					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			int = 0					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			int = None					,):
									UpperCAmelCase__    =							self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
									UpperCAmelCase__    =							self.get_auto_remove_tmp_dir()
									UpperCAmelCase__    =							f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(lowerCamelCase__    )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCamelCase__    )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

									UpperCAmelCase__    =							f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCamelCase__    )}
        '''.split()

									UpperCAmelCase__    =							'\n            --do_predict\n        '.split()

									UpperCAmelCase__    =							[]
									if do_train:
													args += args_train

									if do_eval:
													args += args_eval

									if do_predict:
													args += args_predict

									if predict_with_generate:
													args += "--predict_with_generate".split()

									if do_train:
													if optim == "adafactor":
																	args += "--adafactor".split()
													else:
																	args += f'''--optim {optim}'''.split()

									if extra_args_str is not None:
													args += extra_args_str.split()

									if distributed:
													if n_gpus_to_use is None:
																	UpperCAmelCase__    =							get_gpu_count()
													UpperCAmelCase__    =							get_torch_dist_unique_port()
													UpperCAmelCase__    =							f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
													UpperCAmelCase__    =							[sys.executable] + distributed_args + args
													# keep for quick debug
													# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
													execute_subprocess_async(lowerCamelCase__					,env=self.get_env()    )
									else:
													UpperCAmelCase__    =							['run_translation.py'] + args
													with patch.object(lowerCamelCase__					,'argv'					,lowerCamelCase__    ):
																	main()

									return output_dir




"""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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __get__(       self     :			Dict					,lowerCamelCase__     :			str					,lowerCamelCase__     :			str=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'    )
									UpperCAmelCase__    =							'__cached_' + self.fget.__name__
									UpperCAmelCase__    =							getattr(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )
									if cached is None:
													UpperCAmelCase__    =							self.fget(lowerCamelCase__    )
													setattr(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )
									return cached






def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							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_		(      lowerCamelCase ):
				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_		(      lowerCamelCase ):
				return isinstance(lowerCamelCase      ,    np.ndarray )



def 						a_		(      lowerCamelCase ):
				return _is_numpy(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				import torch

				return isinstance(lowerCamelCase      ,    torch.Tensor )



def 						a_		(      lowerCamelCase ):
				return False if not is_torch_available() else _is_torch(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				import torch

				return isinstance(lowerCamelCase      ,    torch.device )



def 						a_		(      lowerCamelCase ):
				return False if not is_torch_available() else _is_torch_device(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				import torch

				if isinstance(lowerCamelCase      ,    lowerCamelCase ):
								if hasattr(lowerCamelCase      ,    lowerCamelCase ):
												UpperCAmelCase__    =							getattr(lowerCamelCase      ,    lowerCamelCase )
								else:
												return False
				return isinstance(lowerCamelCase      ,    torch.dtype )



def 						a_		(      lowerCamelCase ):
				return False if not is_torch_available() else _is_torch_dtype(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				import tensorflow as tf

				return isinstance(lowerCamelCase      ,    tf.Tensor )



def 						a_		(      lowerCamelCase ):
				return False if not is_tf_available() else _is_tensorflow(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				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_		(      lowerCamelCase ):
				return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				import jax.numpy as jnp  # noqa: F811

				return isinstance(lowerCamelCase      ,    jnp.ndarray )



def 						a_		(      lowerCamelCase ):
				return False if not is_flax_available() else _is_jax(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				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_		(      lowerCamelCase ):
				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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							fields(self    )

									# Safety and consistency checks
									if not len(lowerCamelCase__    ):
													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.'''    )

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

									if other_fields_are_none and not is_tensor(lowerCamelCase__    ):
													if isinstance(lowerCamelCase__					,lowerCamelCase__    ):
																	UpperCAmelCase__    =							first_field.items()
																	UpperCAmelCase__    =							True
													else:
																	try:
																					UpperCAmelCase__    =							iter(lowerCamelCase__    )
																					UpperCAmelCase__    =							True
																	except TypeError:
																					UpperCAmelCase__    =							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(lowerCamelCase__    ):
																					if (
																					    not isinstance(lowerCamelCase__					,(list, tuple)    )
																					    or not len(lowerCamelCase__    ) == 2
																					    or not isinstance(element[0]					,lowerCamelCase__    )
																					):
																									if idx == 0:
																													# If we do not have an iterator of key/values, set it as attribute
																													UpperCAmelCase__    =							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:
																									UpperCAmelCase__    =							element[1]
													elif first_field is not None:
																	UpperCAmelCase__    =							first_field
									else:
													for field in class_fields:
																	UpperCAmelCase__    =							getattr(self					,field.name    )
																	if v is not None:
																					UpperCAmelCase__    =							v







					def __delitem__(       self     :			List[Any]					,*lowerCamelCase__     :			Any					,**lowerCamelCase__     :			Dict    ):
									raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''    )







					def 	__lowerCAmelCase		(       self     :			str					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			Optional[Any]    ):
									raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			List[Any]    ):
									raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,*lowerCamelCase__     :			List[str]					,**lowerCamelCase__     :			Optional[int]    ):
									raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''    )







					def __getitem__(       self     :			Tuple					,lowerCamelCase__     :			List[Any]    ):
									if isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													UpperCAmelCase__    =							dict(self.items()    )
													return inner_dict[k]
									else:
													return self.to_tuple()[k]







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







					def __setitem__(       self     :			Dict					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Any    ):
									# Will raise a KeyException if needed
									super().__setitem__(lowerCamelCase__					,lowerCamelCase__    )
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									return tuple(self[k] for k in self.keys()    )






class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					@classmethod
					def 	__lowerCAmelCase		(       cls     :			str					,lowerCamelCase__     :			Optional[int]    ):
									raise ValueError(
									    f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()    )}'''    )






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"longest"
					snake_case__       					=				"max_length"
					snake_case__       					=				"do_not_pad"






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"pt"
					snake_case__       					=				"tf"
					snake_case__       					=				"np"
					snake_case__       					=				"jax"






class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			int					,lowerCamelCase__     :			List[ContextManager]    ):
									UpperCAmelCase__    =							context_managers
									UpperCAmelCase__    =							ExitStack()







					def __enter__(       self     :			Union[str, Any]    ):
									for context_manager in self.context_managers:
													self.stack.enter_context(lowerCamelCase__    )







					def __exit__(       self     :			List[Any]					,*lowerCamelCase__     :			List[str]					,**lowerCamelCase__     :			Dict    ):
									self.stack.__exit__(*lowerCamelCase__					,**lowerCamelCase__    )






def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							infer_framework(lowerCamelCase )
				if framework == "tf":
								UpperCAmelCase__    =							inspect.signature(model_class.call )  # TensorFlow models
				elif framework == "pt":
								UpperCAmelCase__    =							inspect.signature(model_class.forward )  # PyTorch models
				else:
								UpperCAmelCase__    =							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_		(      lowerCamelCase ):
				UpperCAmelCase__    =							model_class.__name__
				UpperCAmelCase__    =							infer_framework(lowerCamelCase )
				if framework == "tf":
								UpperCAmelCase__    =							inspect.signature(model_class.call )  # TensorFlow models
				elif framework == "pt":
								UpperCAmelCase__    =							inspect.signature(model_class.forward )  # PyTorch models
				else:
								UpperCAmelCase__    =							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_		(      lowerCamelCase      ,    lowerCamelCase = ""      ,    lowerCamelCase = "." ):

				def _flatten_dict(lowerCamelCase      ,    lowerCamelCase=""      ,    lowerCamelCase="." ):
								for k, v in d.items():
												UpperCAmelCase__    =							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_		(      lowerCamelCase      ,    lowerCamelCase = False ):
				if use_temp_dir:
								with tempfile.TemporaryDirectory() as tmp_dir:
												yield tmp_dir
				else:
								yield working_dir



def 						a_		(      lowerCamelCase      ,    lowerCamelCase=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_		(      lowerCamelCase      ,    lowerCamelCase ):
				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_		(      lowerCamelCase      ,    lowerCamelCase=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_		(      lowerCamelCase      ,    lowerCamelCase ):
				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_		(      lowerCamelCase ):
				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_		(      lowerCamelCase      ,    lowerCamelCase ):
				for key, value in auto_map.items():
								if isinstance(lowerCamelCase      ,    (tuple, list) ):
												UpperCAmelCase__    =							[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:
												UpperCAmelCase__    =							f'''{repo_id}--{value}'''

				return auto_map







def 						a_		(      lowerCamelCase ):
				for base_class in inspect.getmro(lowerCamelCase ):
								UpperCAmelCase__    =							base_class.__module__
								UpperCAmelCase__    =							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}.''' )



