

'''simple docstring'''
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 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    Union[str, Any]=1_0	)		->	List[Any]:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    str=1_0	)		->	Dict:




  '''simple docstring'''



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

        lowercase									=torch.load(lowercase_	)
        scheduler.load_state_dict(lowercase_	)
  return lrs

@require_torch
class 						__magic_name__   (							unittest.TestCase ):







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







 def 						_A(       self   ):
   lowercase									=torch.tensor([0.1, -0.2, -0.1]  , requires_grad=snake_case_   )
   lowercase									=torch.tensor([0.4, 0.2, -0.5]   )
   lowercase									=nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowercase									=AdamW(params=[w]  , lr=2E-1  , weight_decay=0.0   )
   for _ in range(1_00   ):
     lowercase									=criterion(snake_case_  , snake_case_   )
     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 						_A(       self   ):
   lowercase									=torch.tensor([0.1, -0.2, -0.1]  , requires_grad=snake_case_   )
   lowercase									=torch.tensor([0.4, 0.2, -0.5]   )
   lowercase									=nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowercase									=Adafactor(
       params=[w]  , lr=1E-2  , eps=(1E-30, 1E-3)  , clip_threshold=1.0  , decay_rate=-0.8  , betaa=snake_case_  , weight_decay=0.0  , relative_step=snake_case_  , scale_parameter=snake_case_  , warmup_init=snake_case_  , )
   for _ in range(10_00   ):
     lowercase									=criterion(snake_case_  , snake_case_   )
     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 						__magic_name__   (							unittest.TestCase ):
 UpperCamelCase__     =       nn.Linear(50 ,				50 ) if is_torch_available() else None
 UpperCamelCase__     =       AdamW(m.parameters() ,				lr=1_0.0 ) if is_torch_available() else None
 UpperCamelCase__     =       10







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_=None   ):
   self.assertEqual(len(snake_case_   )  , len(snake_case_   )   )
   for a, b in zip(snake_case_  , snake_case_   ):
     self.assertAlmostEqual(snake_case_  , snake_case_  , delta=snake_case_  , msg=snake_case_   )







 def 						_A(       self   ):
   lowercase									={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowercase									={
       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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
       ),
       get_inverse_sqrt_schedule: (
           {'''num_warmup_steps''': 2},
           [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
       ),
   }

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

     lowercase									=scheduler_func(self.optimizer  , **snake_case_   )
     self.assertEqual(len([scheduler.get_lr()[0]]   )  , 1   )
     lowercase									=unwrap_schedule(snake_case_  , self.num_steps   )
     self.assertListAlmostEqual(
         snake_case_  , snake_case_  , tol=1E-2  , msg=f'failed for {scheduler_func} in normal scheduler'  , )

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

class 						__magic_name__   :







 def __init__(       self  , snake_case_   ):
   lowercase									=fn







 def __call__(       self  , *snake_case_  , **snake_case_   ):
   return self.fn(*snake_case_  , **snake_case_   )







 @classmethod
 def 						_A(       self  , snake_case_   ):
   lowercase									=list(map(self  , scheduler.lr_lambdas   )   )


import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class   __lowercase						(						__snake_case       ):






	def __init__(self				: List[Any]      ,	snake_case				: Optional[int]      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	Tuple           =   dataset
							_lowercase       :	List[str]           =   process
							_lowercase       :	Any           =   params






	def __len__(self				: Optional[Any]    ) ->			Any:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			int:
							_lowercase       :	Optional[Any]           =   self.dataset[i]
							_lowercase       :	Union[str, Any]           =   self.process(snake_case      ,	**self.params    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: List[Any]      ,	snake_case				: Tuple      ,	snake_case				: Tuple      ,	snake_case				: List[str]=None    ) ->			Optional[int]:
							_lowercase       :	List[str]           =   loader
							_lowercase       :	Optional[Any]           =   infer
							_lowercase       :	List[Any]           =   params
							if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													_lowercase       :	str           =   None
							_lowercase       :	Optional[int]           =   loader_batch_size

							# Internal bookkeeping
							_lowercase       :	Dict           =   None
							_lowercase       :	Union[str, Any]           =   None






	def __len__(self				: Any    ) ->			Any:
							return len(self.loader    )






	def __iter__(self				: int    ) ->			Optional[Any]:
							_lowercase       :	List[str]           =   iter(self.loader    )
							return self






	def       _a(self				: int    ) ->			List[str]:
							if isinstance(self._loader_batch_data      ,	torch.Tensor    ):
													# Batch data is simple tensor, just fetch the slice
													_lowercase       :	Any           =   self._loader_batch_data[self._loader_batch_index]
							else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													_lowercase       :	List[str]           =   {}
													for k, element in self._loader_batch_data.items():
																			if isinstance(snake_case      ,	snake_case    ):
																									# Convert ModelOutput to tuple first
																									_lowercase       :	Dict           =   element.to_tuple()
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	Optional[Any]           =   tuple(np.expand_dims(el[self._loader_batch_index]      ,	0    ) for el in element    )
																									continue
																			if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case      ,	snake_case    ):
																									# Those are stored as lists of tensors so need specific unbatching.
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	List[str]           =   tuple(np.expand_dims(el[self._loader_batch_index]      ,	0    ) for el in element    )
																									continue
																			if element is None:
																									# This can happen for optional data that get passed around
																									_lowercase       :	Optional[Any]           =   None
																			elif isinstance(element[self._loader_batch_index]      ,	torch.Tensor    ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers

																									_lowercase       :	int           =   element[self._loader_batch_index].unsqueeze(0    )
																			elif isinstance(element[self._loader_batch_index]      ,	np.ndarray    ):
																									# Take correct batch data, but make it looked like batch_size=1
																									# For compatibility with other methods within transformers
																									_lowercase       :	Tuple           =   np.expand_dims(element[self._loader_batch_index]      ,	0    )
																			else:
																									# This is typically a list, so no need to `unsqueeze`.
																									_lowercase       :	Union[str, Any]           =   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													_lowercase       :	int           =   self._loader_batch_data.__class__(snake_case    )
							self._loader_batch_index += 1
							return result






	def       _a(self				: Any    ) ->			str:
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													# We are currently unrolling a batch so we just need to return
													# the current item within a batch
													return self.loader_batch_item()

							# We're out of items within a batch
							_lowercase       :	str           =   next(self.iterator    )
							_lowercase       :	Any           =   self.infer(snake_case      ,	**self.params    )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
													# Try to infer the size of the batch
													if isinstance(snake_case      ,	torch.Tensor    ):
																			_lowercase       :	int           =   processed
													else:
																			_lowercase       :	List[str]           =   list(processed.keys()    )[0]
																			_lowercase       :	List[str]           =   processed[key]
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	int           =   len(snake_case    )
													else:
																			_lowercase       :	Optional[int]           =   first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																			# could be last batch so we can't unroll as many
																			# elements.
																			_lowercase       :	Union[str, Any]           =   observed_batch_size
													# Setting internal index to unwrap the batch
													_lowercase       :	Any           =   processed
													_lowercase       :	Dict           =   0
													return self.loader_batch_item()
							else:
													# We're not unrolling batches
													return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: Union[str, Any]      ,	snake_case				: str      ,	snake_case				: Dict      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]=None    ) ->			List[str]:
							super().__init__(snake_case      ,	snake_case      ,	snake_case    )






	def __iter__(self				: List[Any]    ) ->			str:
							_lowercase       :	int           =   iter(self.loader    )
							_lowercase       :	Optional[int]           =   None
							return self






	def       _a(self				: Optional[Any]    ) ->			Optional[int]:
							if self.subiterator is None:
													_lowercase       :	List[str]           =   self.infer(next(self.iterator    )      ,	**self.params    )
							try:
													# Try to return next item
													_lowercase       :	str           =   next(self.subiterator    )
							except StopIteration:
													# When a preprocess iterator ends, we can start lookig at the next item
													# ChunkIterator will keep feeding until ALL elements of iterator
													# all have created their subiterator and have been iterating against.
													#
													# Another way to look at it, is we're basically flattening lists of lists
													# into a single list, but with generators
													_lowercase       :	Any           =   self.infer(next(self.iterator    )      ,	**self.params    )
													_lowercase       :	Optional[int]           =   next(self.subiterator    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __iter__(self				: List[str]    ) ->			List[str]:
							_lowercase       :	Any           =   iter(self.loader    )
							return self






	def       _a(self				: List[Any]    ) ->			Union[str, Any]:
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							_lowercase       :	Dict           =   False
							_lowercase       :	Optional[int]           =   []
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
													while self._loader_batch_index < self.loader_batch_size:
																			_lowercase       :	Tuple           =   self.loader_batch_item()
																			_lowercase       :	List[Any]           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
																			if is_last:
																									return accumulator

							while not is_last:
													_lowercase       :	List[Any]           =   self.infer(next(self.iterator    )      ,	**self.params    )
													if self.loader_batch_size is not None:
																			if isinstance(snake_case      ,	torch.Tensor    ):
																									_lowercase       :	Optional[Any]           =   processed
																			else:
																									_lowercase       :	Tuple           =   list(processed.keys()    )[0]
																									_lowercase       :	int           =   processed[key]
																			if isinstance(snake_case      ,	snake_case    ):
																									_lowercase       :	str           =   len(snake_case    )
																			else:
																									_lowercase       :	List[str]           =   first_tensor.shape[0]
																			if 0 < observed_batch_size < self.loader_batch_size:
																									# could be last batch so we can't unroll as many
																									# elements.
																									_lowercase       :	Tuple           =   observed_batch_size
																			_lowercase       :	Any           =   processed
																			_lowercase       :	Dict           =   0
																			while self._loader_batch_index < self.loader_batch_size:
																									_lowercase       :	str           =   self.loader_batch_item()
																									_lowercase       :	int           =   item.pop("is_last"    )
																									accumulator.append(snake_case    )
																									if is_last:
																															return accumulator
													else:
																			_lowercase       :	str           =   processed
																			_lowercase       :	int           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
							return accumulator




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   dataset
							_lowercase       :	Any           =   key






	def __len__(self				: Any    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			Any:
							return self.dataset[i][self.key]




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	int           =   dataset
							_lowercase       :	Optional[Any]           =   keya
							_lowercase       :	Tuple           =   keya






	def __len__(self				: List[str]    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: Optional[Any]      ,	snake_case				: Dict    ) ->			int:
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




