




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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=10						)   ->       int:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=10						)   ->       List[str]:



       """simple docstring"""

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

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


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[int])						->Optional[int]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__       =    torch.tensor([0.1, -0.2, -0.1]					, requires_grad=UpperCAmelCase__)
           A__       =    torch.tensor([0.4, 0.2, -0.5])
           A__       =    nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           A__       =    AdamW(params=[w]					, lr=2e-1					, weight_decay=0.0)
           for _ in range(100):
                  A__       =    criterion(UpperCAmelCase__					, UpperCAmelCase__)
                  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      :	str)						->Tuple:





           '''simple docstring'''

           A__       =    torch.tensor([0.1, -0.2, -0.1]					, requires_grad=UpperCAmelCase__)
           A__       =    torch.tensor([0.4, 0.2, -0.5])
           A__       =    nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           A__       =    Adafactor(
               params=[w]					, lr=1e-2					, eps=(1e-30, 1e-3)					, clip_threshold=1.0					, decay_rate=-0.8					, betaa=UpperCAmelCase__					, weight_decay=0.0					, relative_step=UpperCAmelCase__					, scale_parameter=UpperCAmelCase__					, warmup_init=UpperCAmelCase__					, )
           for _ in range(1_000):
                  A__       =    criterion(UpperCAmelCase__					, UpperCAmelCase__)
                  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'''






    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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	List[str]=None)						->Any:





           '''simple docstring'''

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





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           A__       =    {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           A__       =    {
               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():
                  A__ ,							A__       =    data

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

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


class 				UpperCamelCase_    :

    '''simple docstring'''






    def __init__(			self      :	int					, UpperCAmelCase__      :	int)						->Tuple:





           '''simple docstring'''

           A__       =    fn


    def __call__(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Optional[int]					, **UpperCAmelCase__      :	Dict)						->List[str]:





           '''simple docstring'''

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





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	str)						->str:





           '''simple docstring'''

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









import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

