'''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( _A       :					int	,      _A       :					Tuple=10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Union[str, Any] 					=	[]
   for _ in range(__lowercase     ):
      lrs.append(scheduler.get_lr()[0]     )
      scheduler.step()
   return lrs







def __UpperCamelCase( _A       :					Any	,      _A       :					Any=10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[Any] 					=	[]
   for step in range(__lowercase     ):
      lrs.append(scheduler.get_lr()[0]     )
      scheduler.step()
      if step == num_steps // 2:
         with tempfile.TemporaryDirectory() as tmpdirname:
            UpperCAmelCase__				:					str 					=	os.path.join(__lowercase	,      '''schedule.bin'''     )
            torch.save(scheduler.state_dict()	,      __lowercase     )

            UpperCAmelCase__				:					Tuple 					=	torch.load(__lowercase     )
            scheduler.load_state_dict(__lowercase     )
   return lrs


@require_torch
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



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



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	torch.tensor([0.1, -0.2, -0.1]       ,requires_grad=UpperCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	torch.tensor([0.4, 0.2, -0.5]				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         UpperCAmelCase__				:					List[str] 					=	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(1000				):
            UpperCAmelCase__				:					Dict 					=	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 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[Any]   				= nn.Linear(50		,50 ) if is_torch_available() else None
      UpperCAmelCase_   :   Tuple   				= AdamW(m.parameters()		,lr=1_0.0 ) if is_torch_available() else None
      UpperCAmelCase_   :   Any   				= 10




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
         # schedulers doct format
         # function: (sched_args_dict, expected_learning_rates)
         UpperCAmelCase__				:					Optional[int] 					=	{
             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():
            UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	data

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

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




class 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	fn




      def __call__(					self       ,*lowerCamelCase_       ,**lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



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




      @classmethod
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	list(map(self       ,scheduler.lr_lambdas				)				)












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search("""\\s"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

    __A       =						cached_file(
        __lowercase      ,					PROMPT_FILES[mode]      ,					repo_type="""dataset"""      ,					user_agent={"""agent""": agent_name}     )
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""     ) as f:
        return f.read()




