





'''simple docstring'''



import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
 import onnxruntime as ort


__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase=None						,   **lowerCamelCase	):
       logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."	)
       _snake_case     		=				model
       _snake_case     		=				kwargs.get("model_save_dir"						,   UpperCamelCase__	)
       _snake_case     		=				kwargs.get("latest_model_name"						,   UpperCamelCase__	)

     def __call__(					self						,   **lowerCamelCase	):
       _snake_case     		=				{k: np.array(UpperCamelCase__	) for k, v in kwargs.items()}
       return self.model.run(UpperCamelCase__						,   UpperCamelCase__	)

     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase=None						,   lowerCamelCase=None	):





       if provider is None:
         logger.info("No onnxruntime provider specified, using CPUExecutionProvider"	)
         _snake_case     		=				"CPUExecutionProvider"

       return ort.InferenceSession(UpperCamelCase__						,   providers=[provider]						,   sess_options=UpperCamelCase__	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase	):
       _snake_case     		=				file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _snake_case     		=				self.model_save_dir.joinpath(self.latest_model_name	)
       _snake_case     		=				Path(UpperCamelCase__	).joinpath(UpperCamelCase__	)
       try:
         shutil.copyfile(UpperCamelCase__						,   UpperCamelCase__	)
       except shutil.SameFileError:
         pass

       # copy external weights (for models >2GB)
       _snake_case     		=				self.model_save_dir.joinpath(UpperCamelCase__	)
       if src_path.exists():
         _snake_case     		=				Path(UpperCamelCase__	).joinpath(UpperCamelCase__	)
         try:
           shutil.copyfile(UpperCamelCase__						,   UpperCamelCase__	)
         except shutil.SameFileError:
           pass

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   **lowerCamelCase						,   ):





       if os.path.isfile(UpperCamelCase__	):
         logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''	)
         return

       os.makedirs(UpperCamelCase__						,   exist_ok=UpperCamelCase__	)

       # saving model weights/files
       self._save_pretrained(UpperCamelCase__						,   **UpperCamelCase__	)

     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = False						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(UpperCamelCase__	):
         _snake_case     		=				OnnxRuntimeModel.load_model(
             os.path.join(UpperCamelCase__						,   UpperCamelCase__	)						,   provider=UpperCamelCase__						,   sess_options=UpperCamelCase__	)
         _snake_case     		=				Path(UpperCamelCase__	)
       # load model from hub
       else:
         # download model
         _snake_case     		=				hf_hub_download(
             repo_id=UpperCamelCase__						,   filename=UpperCamelCase__						,   use_auth_token=UpperCamelCase__						,   revision=UpperCamelCase__						,   cache_dir=UpperCamelCase__						,   force_download=UpperCamelCase__						,   )
         _snake_case     		=				Path(UpperCamelCase__	).parent
         _snake_case     		=				Path(UpperCamelCase__	).name
         _snake_case     		=				OnnxRuntimeModel.load_model(UpperCamelCase__						,   provider=UpperCamelCase__						,   sess_options=UpperCamelCase__	)
       return cls(model=UpperCamelCase__						,   **UpperCamelCase__	)






     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				None
       if len(str(UpperCamelCase__	).split("@"	)	) == 2:
         _snake_case		,		_snake_case     		=				model_id.split("@"	)

       return cls._from_pretrained(
           model_id=UpperCamelCase__						,   revision=UpperCamelCase__						,   cache_dir=UpperCamelCase__						,   force_download=UpperCamelCase__						,   use_auth_token=UpperCamelCase__						,   **UpperCamelCase__						,   )









'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  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:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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'''






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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           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():
         _snake_case		,		_snake_case     		=				data

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

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







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

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






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


