

from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def      snake_case_				(__A					:						str ,       __A					:						List[str] ,       __A					:						Tuple=None ,       __A					:						Union[str, Any]=None					)      ->     Optional[Any]:
   if attention_mask is None:
      __lowerCAmelCase  :      Optional[int]    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=OPTConfig
  lowerCamelCase   :      int										={}
  lowerCamelCase   :      Optional[Any]										="gelu"







  def __init__(      self			:   Optional[Any]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict=13	,							lowerCAmelCase			:   Optional[int]=7	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   str=False	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   Optional[Any]=16	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   Union[str, Any]=4	,							lowerCAmelCase			:   Dict=4	,							lowerCAmelCase			:   Optional[Any]="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   List[Any]=0.1	,							lowerCAmelCase			:   str=20	,							lowerCAmelCase			:   Union[str, Any]=2	,							lowerCAmelCase			:   Dict=1	,							lowerCAmelCase			:   Optional[Any]=0	,							lowerCAmelCase			:   str=16	,							lowerCAmelCase			:   Dict=16	,							)							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      Union[str, Any]    =						seq_length
     __lowerCAmelCase  :      List[Any]    =						is_training
     __lowerCAmelCase  :      Union[str, Any]    =						use_labels
     __lowerCAmelCase  :      Tuple    =						vocab_size
     __lowerCAmelCase  :      Optional[Any]    =						hidden_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_hidden_layers
     __lowerCAmelCase  :      Union[str, Any]    =						num_attention_heads
     __lowerCAmelCase  :      Union[str, Any]    =						intermediate_size
     __lowerCAmelCase  :      List[str]    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      int    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings
     __lowerCAmelCase  :      int    =						eos_token_id
     __lowerCAmelCase  :      str    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id
     __lowerCAmelCase  :      Union[str, Any]    =						embed_dim
     __lowerCAmelCase  :      int    =						word_embed_proj_dim
     __lowerCAmelCase  :      Optional[Any]    =						False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size   )
     __lowerCAmelCase  :      List[str]    =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,							1   )
     __lowerCAmelCase  :      Union[str, Any]    =						tf.concat([input_ids, eos_tensor]	,							axis=1   )

     __lowerCAmelCase  :      Tuple    =						self.config_cls(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							ffn_dim=self.intermediate_size	,							dropout=self.hidden_dropout_prob	,							attention_dropout=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							eos_token_id=self.eos_token_id	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							embed_dim=self.embed_dim	,							word_embed_proj_dim=self.word_embed_proj_dim	,							is_encoder_decoder=lowerCAmelCase	,							**self.config_updates	,							)
     __lowerCAmelCase  :      Optional[int]    =						prepare_opt_inputs_dict(lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFOPTModel(config=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      List[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      str    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Dict    =						1

     # first forward pass
     __lowerCAmelCase  :      Union[str, Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							use_cache=lowerCAmelCase   )

     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     __lowerCAmelCase  :      List[str]    =						ids_tensor((self.batch_size, 3)	,							config.vocab_size   )
     __lowerCAmelCase  :      Optional[Any]    =						tf.cast(ids_tensor((self.batch_size, 3)	,							2   )	,							tf.inta   )

     # append to next input_ids and
     __lowerCAmelCase  :      Optional[Any]    =						tf.concat([input_ids, next_tokens]	,							axis=-1   )
     __lowerCAmelCase  :      Any    =						tf.concat([attention_mask, next_attn_mask]	,							axis=-1   )

     __lowerCAmelCase  :      int    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase   )[0]
     __lowerCAmelCase  :      Optional[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							past_key_values=lowerCAmelCase   )[0]

     self.parent.assertEqual(next_tokens.shape[1]	,							output_from_past.shape[1]   )

     # select random slice
     __lowerCAmelCase  :      Optional[Any]    =						int(ids_tensor((1,)	,							output_from_past.shape[-1]   )   )
     __lowerCAmelCase  :      str    =						output_from_no_past[:, -3:, random_slice_idx]
     __lowerCAmelCase  :      Any    =						output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowerCAmelCase	,							lowerCAmelCase	,							rtol=1e-3   )






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






  lowerCamelCase   :      Union[str, Any]										=(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  lowerCamelCase   :      Dict										=(TFOPTForCausalLM,) if is_tf_available() else ()
  lowerCamelCase   :      Tuple										=(
      {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
  )
  lowerCamelCase   :      Optional[int]										=False
  lowerCamelCase   :      Any										=False
  lowerCamelCase   :      Optional[int]										=False
  lowerCamelCase   :      Dict										=10







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFOPTModelTester(self   )
     __lowerCAmelCase  :      Tuple    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Any   ):
        if hasattr(lowerCAmelCase	,							"""weight"""   ):
           return embedding_layer.weight
        else:
           # Here we build the word embeddings weights if not exists.
           # And then we retry to get the attribute once built.
           model.build()
           if hasattr(lowerCAmelCase	,							"""weight"""   ):
              return embedding_layer.weight
           else:
              return None

     for model_class in self.all_model_classes:
        for size in [config.vocab_size - 10, config.vocab_size + 10]:
           # build the embeddings
           __lowerCAmelCase  :      List[Any]    =						model_class(config=lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[Any]    =						_get_word_embedding_weight(lowerCAmelCase	,							model.get_input_embeddings()   )
           __lowerCAmelCase  :      int    =						_get_word_embedding_weight(lowerCAmelCase	,							model.get_output_embeddings()   )

           # reshape the embeddings
           model.resize_token_embeddings(lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[Any]    =						_get_word_embedding_weight(lowerCAmelCase	,							model.get_input_embeddings()   )
           __lowerCAmelCase  :      Any    =						_get_word_embedding_weight(lowerCAmelCase	,							model.get_output_embeddings()   )

           # check that the resized embeddings size matches the desired size.
           __lowerCAmelCase  :      Dict    =						size if size is not None else config.vocab_size

           self.assertEqual(new_input_embeddings.shape[0]	,							lowerCAmelCase   )

           # check that weights remain the same after resizing
           __lowerCAmelCase  :      int    =						True
           for pa, pa in zip(old_input_embeddings.value()	,							new_input_embeddings.value()   ):
              if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                 __lowerCAmelCase  :      Dict    =						False
           self.assertTrue(lowerCAmelCase   )

           if old_output_embeddings is not None and new_output_embeddings is not None:
              self.assertEqual(new_output_embeddings.shape[0]	,							lowerCAmelCase   )

              __lowerCAmelCase  :      int    =						True
              for pa, pa in zip(old_output_embeddings.value()	,							new_output_embeddings.value()   ):
                 if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                    __lowerCAmelCase  :      Union[str, Any]    =						False
              self.assertTrue(lowerCAmelCase   )






def      snake_case_				(__A					:						Optional[int]					)      ->     Union[str, Any]:
   return tf.constant(__A ,       dtype=tf.intaa					)



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






  lowerCamelCase   :      List[str]										=99







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						tf.ones((4, 1)	,							dtype=tf.intaa   ) * 2
     __lowerCAmelCase  :      Dict    =						tf.concat([ids_tensor((4, 6)	,							self.vocab_size - 3   ) + 3, eos_column_vector]	,							axis=1   )
     __lowerCAmelCase  :      Union[str, Any]    =						input_ids.shape[0]
     __lowerCAmelCase  :      Optional[Any]    =						OPTConfig(
         vocab_size=self.vocab_size	,							hidden_size=24	,							num_hidden_layers=2	,							num_attention_heads=2	,							ffn_dim=32	,							max_position_embeddings=48	,							eos_token_id=2	,							pad_token_id=1	,							bos_token_id=0	,							)
     return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						TFOPTModel.from_pretrained("""facebook/opt-350m"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
     __lowerCAmelCase  :      List[Any]    =						tf.not_equal(lowerCAmelCase	,							model.config.pad_token_id   )
     with tf.GradientTape():
        __lowerCAmelCase  :      Optional[Any]    =						model(input_ids=lowerCAmelCase	,							attention_mask=lowerCAmelCase   ).last_hidden_state
     __lowerCAmelCase  :      str    =						(1, 11, 5_12)
     self.assertEqual(output.shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						tf.constant(
         [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
     self.assertTrue(np.allclose(output[:, :3, :3]	,							lowerCAmelCase	,							atol=4e-3   )   )

     __lowerCAmelCase  :      Any    =						tf.function(lowerCAmelCase	,							jit_compile=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						xla_generate(lowerCAmelCase	,							lowerCAmelCase   )[0]
     self.assertTrue(np.allclose(output[:, :3, :3]	,							lowerCAmelCase	,							atol=4e-2   )   )






@require_tf
@slow
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     super().setUp()
     __lowerCAmelCase  :      Tuple    =						"""facebook/opt-350m"""







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFOPTForCausalLM.from_pretrained(self.path_model   )
     __lowerCAmelCase  :      Tuple    =						GPTaTokenizer.from_pretrained(self.path_model   )

     __lowerCAmelCase  :      int    =						[
         """Today is a beautiful day and I want to""",
         """In the city of""",
         """Paris is the capital of France and""",
         """Computers and mobile phones have taken""",
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     __lowerCAmelCase  :      List[Any]    =						tokenizer(lowerCAmelCase	,							return_tensors="""tf"""	,							padding=lowerCAmelCase	,							add_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						tf.math.reduce_mean(model(inputs.input_ids	,							attention_mask=inputs.attention_mask   )[0]	,							axis=-1   )
     __lowerCAmelCase  :      Union[str, Any]    =						tf.constant(
         [
             [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
             [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
             [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
             [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
         ]   )
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-4   )   )

     __lowerCAmelCase  :      Dict    =						tf.function(lowerCAmelCase	,							jit_compile=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						tf.math.reduce_mean(xla_generate(inputs.input_ids	,							attention_mask=inputs.attention_mask   )[0]	,							axis=-1   )
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-4   )   )






@require_tf
@slow
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






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






     """simple docstring"""







     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						"""facebook/opt-125m"""

     __lowerCAmelCase  :      List[str]    =						[
         """Today is a beautiful day and I want to""",
         """In the city of New York, the city""",
         """Paris is the capital of France and the capital""",
         """Computers and mobile phones have taken over the""",
     ]

     __lowerCAmelCase  :      str    =						[]
     __lowerCAmelCase  :      List[Any]    =						GPTaTokenizer.from_pretrained(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						TFOPTForCausalLM.from_pretrained(lowerCAmelCase   )

     for prompt in self.prompts:
        __lowerCAmelCase  :      List[Any]    =						tokenizer(lowerCAmelCase	,							return_tensors="""tf"""   ).input_ids

        __lowerCAmelCase  :      str    =						model.generate(lowerCAmelCase	,							max_length=10   )

        __lowerCAmelCase  :      Dict    =						tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						"""facebook/opt-350m"""

     __lowerCAmelCase  :      int    =						GPTaTokenizer.from_pretrained(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						TFOPTForCausalLM.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						"""left"""

     # use different length sentences to test batching
     __lowerCAmelCase  :      Any    =						[
         """Hello, my dog is a little""",
         """Today, I""",
     ]

     __lowerCAmelCase  :      int    =						tokenizer(lowerCAmelCase	,							return_tensors="""tf"""	,							padding=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						inputs["""input_ids"""]

     __lowerCAmelCase  :      Any    =						model.generate(input_ids=lowerCAmelCase	,							attention_mask=inputs["""attention_mask"""]   )

     __lowerCAmelCase  :      List[Any]    =						tokenizer(sentences[0]	,							return_tensors="""tf"""   ).input_ids
     __lowerCAmelCase  :      Tuple    =						model.generate(input_ids=lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						inputs_non_padded.shape[-1] - tf.math.reduce_sum(
         tf.cast(inputs["""attention_mask"""][-1]	,							tf.intaa   )   )
     __lowerCAmelCase  :      List[Any]    =						tokenizer(sentences[1]	,							return_tensors="""tf"""   ).input_ids
     __lowerCAmelCase  :      Optional[int]    =						model.generate(input_ids=lowerCAmelCase	,							max_length=model.config.max_length - num_paddings   )

     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.decode(output_non_padded[0]	,							skip_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						tokenizer.decode(output_padded[0]	,							skip_special_tokens=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						[
         """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
         """Today, I was in the middle of a conversation with a friend about the""",
     ]
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							[non_padded_sentence, padded_sentence]   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""facebook/opt-350m"""

     __lowerCAmelCase  :      Dict    =						[
         """Today is a beautiful day and I want to""",
         """In the city of San Francisco, the city""",
         """Paris is the capital of France and the capital""",
         """Computers and mobile phones have taken over the""",
     ]

     __lowerCAmelCase  :      Any    =						[]
     __lowerCAmelCase  :      Optional[Any]    =						GPTaTokenizer.from_pretrained(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						TFOPTForCausalLM.from_pretrained(lowerCAmelCase   )

     for prompt in self.prompts:
        __lowerCAmelCase  :      Any    =						tokenizer(lowerCAmelCase	,							return_tensors="""tf"""   ).input_ids

        __lowerCAmelCase  :      Union[str, Any]    =						model.generate(lowerCAmelCase	,							max_length=10   )

        __lowerCAmelCase  :      Any    =						tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase	,							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"""   )


