





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase				:      Tuple						      ={
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      Union[str, Any]						      =["LlamaTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =["LlamaTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      Optional[int]						      =[
      "LlamaForCausalLM",
      "LlamaModel",
      "LlamaPreTrainedModel",
      "LlamaForSequenceClassification",
  ]


if TYPE_CHECKING:
  from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_llama import LlamaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_llama_fast import LlamaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
  import sys

  __lowerCAmelCase				:      Optional[Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''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							(				_lowerCamelCase   :   Any		,			_lowerCamelCase   :   Optional[Any]=10			):
    A__							=     []
    for _ in range(_lowerCamelCase			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
    return lrs





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]=10			):
    A__							=     []
    for step in range(_lowerCamelCase			):
        lrs.append(scheduler.get_lr()[0]			)
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                A__							=     os.path.join(_lowerCamelCase		,			"schedule.bin"			)
                torch.save(scheduler.state_dict()		,			_lowerCamelCase			)

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






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Any		,			lowercase_ :List[Any]		,			lowercase_ :Optional[Any]				)->				int:
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     for a, b in zip(lowercase_		,			lowercase_				):
         self.assertAlmostEqual(lowercase_		,			lowercase_		,			delta=lowercase_				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     A__							=     torch.tensor([0.1, -0.2, -0.1]		,			requires_grad=lowercase_				)
     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(1_00				):
         A__							=     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 	UpperCAmelCase_				(							self :Tuple				)->				List[str]:
     A__							=     torch.tensor([0.1, -0.2, -0.1]		,			requires_grad=lowercase_				)
     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=lowercase_		,			weight_decay=0.0		,			relative_step=lowercase_		,			scale_parameter=lowercase_		,			warmup_init=lowercase_		,			)
     for _ in range(10_00				):
         A__							=     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							):
 __lowercase  						=							nn.Linear(50     ,			50							) if is_torch_available() else None
 __lowercase  						=							AdamW(m.parameters()     ,			lr=1_0.0							) if is_torch_available() else None
 __lowercase  						=							10
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Any		,			lowercase_ :List[Any]		,			lowercase_ :List[Any]		,			lowercase_ :str=None				)->				Optional[int]:
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     for a, b in zip(lowercase_		,			lowercase_				):
         self.assertAlmostEqual(lowercase_		,			lowercase_		,			delta=lowercase_		,			msg=lowercase_				)



 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Any:
     A__							=     {"num_warmup_steps": 2, "num_training_steps": 10}
     # schedulers doct format
     # function: (sched_args_dict, expected_learning_rates)
     A__							=     {
         get_constant_schedule: ({}, [1_0.0] * self.num_steps),
         get_constant_schedule_with_warmup: (
             {"num_warmup_steps": 4},
             [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
         ),
         get_linear_schedule_with_warmup: (
             {**common_kwargs},
             [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
         ),
         get_cosine_schedule_with_warmup: (
             {**common_kwargs},
             [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
         ),
         get_cosine_with_hard_restarts_schedule_with_warmup: (
             {**common_kwargs, "num_cycles": 2},
             [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
         ),
         get_polynomial_decay_schedule_with_warmup: (
             {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
             [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
         ),
         get_inverse_sqrt_schedule: (
             {"num_warmup_steps": 2},
             [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
         ),
     }

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

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

         A__							=     scheduler_func(self.optimizer		,			**lowercase_				)
         if scheduler_func.__name__ != "get_constant_schedule":
             LambdaScheduleWrapper.wrap_scheduler(lowercase_				)  # wrap to test picklability of the schedule
         A__							=     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      :
 def __init__(							self :str		,			lowercase_ :List[str]				)->				Tuple:
     A__							=     fn
 def __call__(							self :List[Any]		,			*lowercase_ :Dict		,			**lowercase_ :Dict				)->				Tuple:
     return self.fn(*lowercase_		,			**lowercase_				)



 @classmethod
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Tuple				)->				List[Any]:
     A__							=     list(map(self		,			scheduler.lr_lambdas				)				)



