





import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=10			)				->     List[str]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		[]
						for _ in range(_SCREAMING_SNAKE_CASE			):
												lrs.append(scheduler.get_lr()[0]			)
												scheduler.step()
						return lrs



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=10			)				->     Optional[int]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		[]
						for step in range(_SCREAMING_SNAKE_CASE			):
												lrs.append(scheduler.get_lr()[0]			)
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE	=		os.path.join(_SCREAMING_SNAKE_CASE				,		"""schedule.bin"""			)
																								torch.save(scheduler.state_dict()				,		_SCREAMING_SNAKE_CASE			)

																								SCREAMING_SNAKE_CASE	=		torch.load(_SCREAMING_SNAKE_CASE			)
																								scheduler.load_state_dict(_SCREAMING_SNAKE_CASE			)
						return lrs

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[Any] )  ->	Optional[Any]:




											'''simple docstring'''


											self.assertEqual(len(_lowercase )		,len(_lowercase ) )
											for a, b in zip(_lowercase		,_lowercase ):
																	self.assertAlmostEqual(_lowercase		,_lowercase		,delta=_lowercase )






					def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		torch.tensor([0.1, -0.2, -0.1]		,requires_grad=_lowercase )
											SCREAMING_SNAKE_CASE	=		torch.tensor([0.4, 0.2, -0.5] )
											SCREAMING_SNAKE_CASE	=		nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											SCREAMING_SNAKE_CASE	=		AdamW(params=[w]		,lr=2e-1		,weight_decay=0.0 )
											for _ in range(100 ):
																	SCREAMING_SNAKE_CASE	=		criterion(_lowercase		,_lowercase )
																	loss.backward()
																	optimizer.step()
																	w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																	w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()		,[0.4, 0.2, -0.5]		,tol=1e-2 )






					def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		torch.tensor([0.1, -0.2, -0.1]		,requires_grad=_lowercase )
											SCREAMING_SNAKE_CASE	=		torch.tensor([0.4, 0.2, -0.5] )
											SCREAMING_SNAKE_CASE	=		nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											SCREAMING_SNAKE_CASE	=		Adafactor(
											    params=[w]		,lr=1e-2		,eps=(1e-3_0, 1e-3)		,clip_threshold=1.0		,decay_rate=-0.8		,betaa=_lowercase		,weight_decay=0.0		,relative_step=_lowercase		,scale_parameter=_lowercase		,warmup_init=_lowercase		,)
											for _ in range(1000 ):
																	SCREAMING_SNAKE_CASE	=		criterion(_lowercase		,_lowercase )
																	loss.backward()
																	optimizer.step()
																	w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																	w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()		,[0.4, 0.2, -0.5]		,tol=1e-2 )

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



					'''simple docstring'''

					__snake_case					:			Optional[int]				=    nn.Linear(50							,   50     ) if is_torch_available() else None
					__snake_case					:			Optional[Any]				=    AdamW(m.parameters()							,   lr=10.0     ) if is_torch_available() else None
					__snake_case					:			Optional[Any]				=    10






					def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					str=None )  ->	Union[str, Any]:




											'''simple docstring'''


											self.assertEqual(len(_lowercase )		,len(_lowercase ) )
											for a, b in zip(_lowercase		,_lowercase ):
																	self.assertAlmostEqual(_lowercase		,_lowercase		,delta=_lowercase		,msg=_lowercase )






					def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		{'num_warmup_steps': 2, 'num_training_steps': 10}
											# schedulers doct format
											# function: (sched_args_dict, expected_learning_rates)
											SCREAMING_SNAKE_CASE	=		{
											    get_constant_schedule: ({}, [10.0] * self.num_steps),
											    get_constant_schedule_with_warmup: (
											        {'num_warmup_steps': 4},
											        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
											    ),
											    get_linear_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
											    ),
											    get_cosine_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
											    ),
											    get_cosine_with_hard_restarts_schedule_with_warmup: (
											        {**common_kwargs, 'num_cycles': 2},
											        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
											    ),
											    get_polynomial_decay_schedule_with_warmup: (
											        {**common_kwargs, 'power': 2.0, 'lr_end': 1e-7},
											        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
											    ),
											    get_inverse_sqrt_schedule: (
											        {'num_warmup_steps': 2},
											        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
											    ),
											}

											for scheduler_func, data in scheds.items():
																	SCREAMING_SNAKE_CASE	=		data

																	SCREAMING_SNAKE_CASE	=		scheduler_func(self.optimizer		,**_lowercase )
																	self.assertEqual(len([scheduler.get_lr()[0]] )		,1 )
																	SCREAMING_SNAKE_CASE	=		unwrap_schedule(_lowercase		,self.num_steps )
																	self.assertListAlmostEqual(
																	    _lowercase		,_lowercase		,tol=1e-2		,msg=F"""failed for {scheduler_func} in normal scheduler"""		,)

																	SCREAMING_SNAKE_CASE	=		scheduler_func(self.optimizer		,**_lowercase )
																	if scheduler_func.__name__ != "get_constant_schedule":
																							LambdaScheduleWrapper.wrap_scheduler(_lowercase )  # wrap to test picklability of the schedule
																	SCREAMING_SNAKE_CASE	=		unwrap_and_save_reload_schedule(_lowercase		,self.num_steps )
																	self.assertListEqual(_lowercase		,_lowercase		,msg=F"""failed for {scheduler_func} in save and reload""" )

class 	UpperCamelCase__				:



					'''simple docstring'''

					def __init__(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any] )  ->	int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		fn






					def __call__(							self		:					int		,*lowerCamelCase__		:					List[str]		,**lowerCamelCase__		:					Tuple )  ->	List[str]:




											'''simple docstring'''


											return self.fn(*_lowercase		,**_lowercase )






					@classmethod
					def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					List[str] )  ->	Union[str, Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		list(map(self		,scheduler.lr_lambdas ) )









import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      for line in lines:
            SCREAMING_SNAKE_CASE	=		re.sub(r"""#.*"""				,		""""""				,		_SCREAMING_SNAKE_CASE			)  # remove comments
            if line:
                  filtered_lines.append(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		"""\n""".join(_SCREAMING_SNAKE_CASE			)

      # Make a hash from all this code
      SCREAMING_SNAKE_CASE	=		full_str.encode("""utf-8"""			)
      return shaaaa(_SCREAMING_SNAKE_CASE			).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_		     = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_		     = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_		     = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_		     = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


