

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import YolosForObjectDetection, YolosModel
   from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Tuple=13	,							lowerCAmelCase			:   Tuple=[30, 30]	,							lowerCAmelCase			:   Dict=2	,							lowerCAmelCase			:   Optional[Any]=3	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   int=True	,							lowerCAmelCase			:   int=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   int=37	,							lowerCAmelCase			:   Any="gelu"	,							lowerCAmelCase			:   Union[str, Any]=0.1	,							lowerCAmelCase			:   Tuple=0.1	,							lowerCAmelCase			:   Tuple=10	,							lowerCAmelCase			:   int=0.02	,							lowerCAmelCase			:   int=3	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Dict=8	,							lowerCAmelCase			:   str=10	,							)							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						parent
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						patch_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_channels
     __lowerCAmelCase  :      Optional[Any]    =						is_training
     __lowerCAmelCase  :      Any    =						use_labels
     __lowerCAmelCase  :      Optional[Any]    =						hidden_size
     __lowerCAmelCase  :      Any    =						num_hidden_layers
     __lowerCAmelCase  :      Tuple    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      List[str]    =						hidden_act
     __lowerCAmelCase  :      List[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[str]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      str    =						type_sequence_label_size
     __lowerCAmelCase  :      Dict    =						initializer_range
     __lowerCAmelCase  :      Union[str, Any]    =						num_labels
     __lowerCAmelCase  :      Any    =						scope
     __lowerCAmelCase  :      Union[str, Any]    =						n_targets
     __lowerCAmelCase  :      Dict    =						num_detection_tokens
     # we set the expected sequence length (which is used in several tests)
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
     __lowerCAmelCase  :      List[str]    =						(image_size[1] // patch_size) * (image_size[0] // patch_size)
     __lowerCAmelCase  :      Optional[int]    =						num_patches + 1 + self.num_detection_tokens







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]   )

     __lowerCAmelCase  :      Optional[Any]    =						None
     if self.use_labels:
        # labels is a list of Dict (each Dict being the labels for a given example in the batch)
        __lowerCAmelCase  :      Tuple    =						[]
        for i in range(self.batch_size   ):
           __lowerCAmelCase  :      Optional[Any]    =						{}
           __lowerCAmelCase  :      List[str]    =						torch.randint(
               high=self.num_labels	,							size=(self.n_targets,)	,							device=lowerCAmelCase   )
           __lowerCAmelCase  :      List[str]    =						torch.rand(self.n_targets	,							4	,							device=lowerCAmelCase   )
           labels.append(lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						self.get_config()

     return config, pixel_values, labels







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     return YolosConfig(
         image_size=self.image_size	,							patch_size=self.patch_size	,							num_channels=self.num_channels	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							num_detection_tokens=self.num_detection_tokens	,							num_labels=self.num_labels	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						YolosModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Any    =						model(lowerCAmelCase   )
     self.parent.assertEqual(
         result.last_hidden_state.shape	,							(self.batch_size, self.expected_seq_len, self.hidden_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						YolosForObjectDetection(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()

     __lowerCAmelCase  :      Union[str, Any]    =						model(pixel_values=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						model(lowerCAmelCase   )

     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
     self.parent.assertEqual(result.pred_boxes.shape	,							(self.batch_size, self.num_detection_tokens, 4)   )

     __lowerCAmelCase  :      str    =						model(pixel_values=lowerCAmelCase	,							labels=lowerCAmelCase   )

     self.parent.assertEqual(result.loss.shape	,							()   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
     self.parent.assertEqual(result.pred_boxes.shape	,							(self.batch_size, self.num_detection_tokens, 4)   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      str    =						config_and_inputs
     __lowerCAmelCase  :      List[Any]    =						{"""pixel_values""": pixel_values}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  lowerCamelCase   :      int										=(
      {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
  )

  lowerCamelCase   :      Dict										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      Any										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Any=False   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						super()._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase	,							return_labels=lowerCAmelCase   )

     if return_labels:
        if model_class.__name__ == "YolosForObjectDetection":
           __lowerCAmelCase  :      Union[str, Any]    =						[]
           for i in range(self.model_tester.batch_size   ):
              __lowerCAmelCase  :      Any    =						{}
              __lowerCAmelCase  :      Any    =						torch.ones(
                  size=(self.model_tester.n_targets,)	,							device=lowerCAmelCase	,							dtype=torch.long   )
              __lowerCAmelCase  :      List[Any]    =						torch.ones(
                  self.model_tester.n_targets	,							4	,							device=lowerCAmelCase	,							dtype=torch.float   )
              labels.append(lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[int]    =						labels

     return inputs_dict







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						YolosModelTester(self   )
     __lowerCAmelCase  :      str    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							has_text_modality=lowerCAmelCase	,							hidden_size=37   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     pass







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






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      int    =						model_class(lowerCAmelCase   )
        self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)   )
        __lowerCAmelCase  :      List[Any]    =						model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase	,							nn.Linear   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Union[str, Any]    =						model_class(lowerCAmelCase   )
        __lowerCAmelCase  :      int    =						inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase  :      Optional[Any]    =						[*signature.parameters.keys()]

        __lowerCAmelCase  :      Union[str, Any]    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]	,							lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :      int    =						True

     # in YOLOS, the seq_len is different
     __lowerCAmelCase  :      Tuple    =						self.model_tester.expected_seq_len
     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      List[Any]    =						True
        __lowerCAmelCase  :      int    =						False
        __lowerCAmelCase  :      Optional[Any]    =						True
        __lowerCAmelCase  :      Any    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        with torch.no_grad():
           __lowerCAmelCase  :      Optional[Any]    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )
        __lowerCAmelCase  :      Any    =						outputs.attentions
        self.assertEqual(len(lowerCAmelCase   )	,							self.model_tester.num_hidden_layers   )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        __lowerCAmelCase  :      List[str]    =						True
        __lowerCAmelCase  :      List[Any]    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        with torch.no_grad():
           __lowerCAmelCase  :      Any    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )
        __lowerCAmelCase  :      Union[str, Any]    =						outputs.attentions
        self.assertEqual(len(lowerCAmelCase   )	,							self.model_tester.num_hidden_layers   )

        self.assertListEqual(
            list(attentions[0].shape[-3:]   )	,							[self.model_tester.num_attention_heads, seq_len, seq_len]	,							)
        __lowerCAmelCase  :      Union[str, Any]    =						len(lowerCAmelCase   )

        # Check attention is always last and order is fine
        __lowerCAmelCase  :      Union[str, Any]    =						True
        __lowerCAmelCase  :      Dict    =						True
        __lowerCAmelCase  :      Tuple    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        with torch.no_grad():
           __lowerCAmelCase  :      Optional[int]    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )

        __lowerCAmelCase  :      List[str]    =						1
        self.assertEqual(out_len + added_hidden_states	,							len(lowerCAmelCase   )   )

        __lowerCAmelCase  :      Tuple    =						outputs.attentions

        self.assertEqual(len(lowerCAmelCase   )	,							self.model_tester.num_hidden_layers   )
        self.assertListEqual(
            list(self_attentions[0].shape[-3:]   )	,							[self.model_tester.num_attention_heads, seq_len, seq_len]	,							)







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






     """simple docstring"""







     def check_hidden_states_output(lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]   ):
        __lowerCAmelCase  :      str    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase  :      Optional[int]    =						model(**self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )   )

        __lowerCAmelCase  :      Tuple    =						outputs.hidden_states

        __lowerCAmelCase  :      str    =						getattr(
            self.model_tester	,							"""expected_num_hidden_layers"""	,							self.model_tester.num_hidden_layers + 1   )
        self.assertEqual(len(lowerCAmelCase   )	,							lowerCAmelCase   )

        # YOLOS has a different seq_length
        __lowerCAmelCase  :      List[Any]    =						self.model_tester.expected_seq_len

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )	,							[seq_length, self.model_tester.hidden_size]	,							)

     __lowerCAmelCase   ,__lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Dict    =						True
        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCAmelCase  :      Dict    =						True

        check_hidden_states_output(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_object_detection(*lowerCAmelCase   )







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






     """simple docstring"""







     for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase  :      Optional[Any]    =						YolosModel.from_pretrained(lowerCAmelCase   )
        self.assertIsNotNone(lowerCAmelCase   )






def      snake_case_				()      ->     str:
   __lowerCAmelCase  :      Tuple    =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
   return image



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






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






     """simple docstring"""







     return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""   ) if is_vision_available() else None







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""   ).to(lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						self.default_image_processor
     __lowerCAmelCase  :      Dict    =						prepare_img()
     __lowerCAmelCase  :      Dict    =						image_processor(images=lowerCAmelCase	,							return_tensors="""pt"""   ).to(lowerCAmelCase   )

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase  :      List[str]    =						model(inputs.pixel_values   )

     # verify outputs
     __lowerCAmelCase  :      Optional[Any]    =						torch.Size((1, 1_00, 92)   )
     self.assertEqual(outputs.logits.shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						torch.tensor(
         [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]	,							device=lowerCAmelCase	,							)
     __lowerCAmelCase  :      Optional[int]    =						torch.tensor(
         [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]	,							device=lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )
     self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )

     # verify postprocessing
     __lowerCAmelCase  :      List[Any]    =						image_processor.post_process_object_detection(
         lowerCAmelCase	,							threshold=0.3	,							target_sizes=[image.size[::-1]]   )[0]
     __lowerCAmelCase  :      int    =						torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						[75, 75, 17, 63, 17]
     __lowerCAmelCase  :      Tuple    =						torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]   ).to(lowerCAmelCase   )

     self.assertEqual(len(results["""scores"""]   )	,							5   )
     self.assertTrue(torch.allclose(results["""scores"""]	,							lowerCAmelCase	,							atol=1e-4   )   )
     self.assertSequenceEqual(results["""labels"""].tolist()	,							lowerCAmelCase   )
     self.assertTrue(torch.allclose(results["""boxes"""][0, :]	,							lowerCAmelCase   )   )





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
   import transformers





def      snake_case_				(__A					:						int					)      ->     List[Any]:
   random.seed(__A					)
   np.random.seed(__A					)
   torch.manual_seed(__A					)
   torch.cuda.manual_seed_all(__A					)
   # ^^ safe to call this function even if cuda is not available



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 0	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Union[float, int] = 1.0	,							lowerCAmelCase			:   Union[float, int] = 2 / 3	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Dict[str, Any] = None	,							**lowerCAmelCase			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      int    =						parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        __lowerCAmelCase  :      Optional[Any]    =						True

     if kwargs.get("""max_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[Any]    =						"""The `max_value` argument is deprecated. Please use `decay` instead."""
        deprecate("""max_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						kwargs["""max_value"""]

     if kwargs.get("""min_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[int]    =						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
        deprecate("""min_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						kwargs["""min_value"""]

     __lowerCAmelCase  :      Optional[Any]    =						list(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[p.clone().detach() for p in parameters]

     if kwargs.get("""device"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      str    =						"""The `device` argument is deprecated. Please use `to` instead."""
        deprecate("""device"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        self.to(device=kwargs["""device"""]   )

     __lowerCAmelCase  :      List[Any]    =						None

     __lowerCAmelCase  :      Any    =						decay
     __lowerCAmelCase  :      Any    =						min_decay
     __lowerCAmelCase  :      List[Any]    =						update_after_step
     __lowerCAmelCase  :      Optional[Any]    =						use_ema_warmup
     __lowerCAmelCase  :      Union[str, Any]    =						inv_gamma
     __lowerCAmelCase  :      Union[str, Any]    =						power
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      str    =						None  # set in `step()`

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







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






     """simple docstring"""







     if self.model_cls is None:
        raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""   )

     if self.model_config is None:
        raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[Any]    =						self.state_dict()
     state_dict.pop("""shadow_params"""	,							lowerCAmelCase   )

     model.register_to_config(**lowerCAmelCase   )
     self.copy_to(model.parameters()   )
     model.save_pretrained(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        __lowerCAmelCase  :      str    =						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        __lowerCAmelCase  :      Optional[int]    =						(1 + step) / (10 + step)

     __lowerCAmelCase  :      Any    =						min(lowerCAmelCase	,							self.decay   )
     # make sure decay is not smaller than min_decay
     __lowerCAmelCase  :      Union[str, Any]    =						max(lowerCAmelCase	,							self.min_decay   )
     return cur_decay_value







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     __lowerCAmelCase  :      Optional[Any]    =						self.get_decay(self.optimization_step   )
     __lowerCAmelCase  :      Dict    =						decay
     __lowerCAmelCase  :      Tuple    =						1 - decay

     __lowerCAmelCase  :      str    =						contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           __lowerCAmelCase  :      List[Any]    =						deepspeed.zero.GatheredParameters(lowerCAmelCase	,							modifier_rank=lowerCAmelCase   )

        with context_manager():
           if param.requires_grad:
              s_param.sub_(one_minus_decay * (s_param - param)   )
           else:
              s_param.copy_(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						list(lowerCAmelCase   )
     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        param.data.copy_(s_param.to(param.device   ).data   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[Any]=None   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[
         p.to(device=lowerCAmelCase	,							dtype=lowerCAmelCase   ) if p.is_floating_point() else p.to(device=lowerCAmelCase   )
         for p in self.shadow_params
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     dict:






     """simple docstring"""







     return {
         "decay": self.decay,
         "min_decay": self.min_decay,
         "optimization_step": self.optimization_step,
         "update_after_step": self.update_after_step,
         "use_ema_warmup": self.use_ema_warmup,
         "inv_gamma": self.inv_gamma,
         "power": self.power,
         "shadow_params": self.shadow_params,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[param.detach().cpu().clone() for param in parameters]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     if self.temp_stored_params is None:
        raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""   )
     for c_param, param in zip(self.temp_stored_params	,							lowerCAmelCase   ):
        param.data.copy_(c_param.data   )

     # Better memory-wise.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						state_dict.get("""decay"""	,							self.decay   )
     if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("""Decay must be between 0 and 1"""   )

     __lowerCAmelCase  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, Any]    =						state_dict.get("""update_after_step"""	,							self.update_after_step   )
     if not isinstance(self.update_after_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid update_after_step"""   )

     __lowerCAmelCase  :      List[str]    =						state_dict.get("""use_ema_warmup"""	,							self.use_ema_warmup   )
     if not isinstance(self.use_ema_warmup	,							lowerCAmelCase   ):
        raise ValueError("""Invalid use_ema_warmup"""   )

     __lowerCAmelCase  :      Optional[int]    =						state_dict.get("""inv_gamma"""	,							self.inv_gamma   )
     if not isinstance(self.inv_gamma	,							(float, int)   ):
        raise ValueError("""Invalid inv_gamma"""   )

     __lowerCAmelCase  :      Any    =						state_dict.get("""power"""	,							self.power   )
     if not isinstance(self.power	,							(float, int)   ):
        raise ValueError("""Invalid power"""   )

     __lowerCAmelCase  :      Optional[Any]    =						state_dict.get("""shadow_params"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						shadow_params
        if not isinstance(self.shadow_params	,							lowerCAmelCase   ):
           raise ValueError("""shadow_params must be a list"""   )
        if not all(isinstance(lowerCAmelCase	,							torch.Tensor   ) for p in self.shadow_params   ):
           raise ValueError("""shadow_params must all be Tensors"""   )


