


"""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 					a__			(				SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]=1_0				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							[]
  for _ in range(SCREAMING_SNAKE_CASE				):
    lrs.append(scheduler.get_lr()[0]				)
    scheduler.step()
  return lrs
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]=1_0				):


  '''simple docstring'''







  lowerCAmelCase     :     str								=							[]
  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:
        lowerCAmelCase     :     List[str]								=							os.path.join(SCREAMING_SNAKE_CASE				,      "schedule.bin"				)
        torch.save(scheduler.state_dict()				,      SCREAMING_SNAKE_CASE				)

        lowerCAmelCase     :     List[str]								=							torch.load(SCREAMING_SNAKE_CASE				)
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE				)
  return lrs


@require_torch
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         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        lowercase__							(						self						):






         """simple docstring"""





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






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							torch.tensor([0.1, -0.2, -0.1]		,     requires_grad=snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([0.4, 0.2, -0.5]						)
         lowerCAmelCase     :     Optional[Any]								=							nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         lowerCAmelCase     :     List[str]								=							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(1_000						):
           lowerCAmelCase     :     List[str]								=							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       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				str   =nn.Linear(50   ,    50				) if is_torch_available() else None
       a  :				Union[str, Any]   =AdamW(m.parameters()   ,    lr=10.0				) if is_torch_available() else None
       a  :				List[Any]   =10

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=None						):






         """simple docstring"""





         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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							{"num_warmup_steps": 2, "num_training_steps": 10}
         # schedulers doct format
         # function: (sched_args_dict, expected_learning_rates)
         lowerCAmelCase     :     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():
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							data

           lowerCAmelCase     :     List[Any]								=							scheduler_func(self.optimizer		,     **snake_case__						)
           self.assertEqual(len([scheduler.get_lr()[0]]						)		,     1						)
           lowerCAmelCase     :     str								=							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"""		,     )

           lowerCAmelCase     :     Union[str, Any]								=							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
           lowerCAmelCase     :     List[Any]								=							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       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							fn

       def __call__(						self		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





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







       @classmethod
       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)