code: 
  """simple docstring"""
  
  
  
  
  
  import os
  import random
  from pathlib import Path
  from typing import List
  
  import numpy as np
  import torch
  from torch.cuda.amp import GradScaler
  
  from .utils import (
      MODEL_NAME,
      OPTIMIZER_NAME,
      RNG_STATE_NAME,
      SCALER_NAME,
      SCHEDULER_NAME,
      get_pretty_name,
      is_tpu_available,
      is_xpu_available,
      save,
  )
  
  
  if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
  
  from .logging import get_logger
  from .state import PartialState
  
  
  _a     :   Any	 =							get_logger(__name__)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = None,	):
     # Model states
     for i, state in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Union[str, Any]      =					f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Optional[Any]      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        save(__lowerCamelCase,	__lowerCamelCase						)
        logger.info(f"""Model weights saved in {output_model_file}"""						)
     # Optimizer states
     for i, opt in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Optional[int]      =					opt.state_dict()
        UpperCAmelCase_      :      Any      =					f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      List[Any]      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        save(__lowerCamelCase,	__lowerCamelCase						)
        logger.info(f"""Optimizer state saved in {output_optimizer_file}"""						)
     # Scheduler states
     for i, scheduler in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Optional[int]      =					scheduler.state_dict()
        UpperCAmelCase_      :      Tuple      =					f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Optional[Any]      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        save(__lowerCamelCase,	__lowerCamelCase						)
        logger.info(f"""Scheduler state saved in {output_scheduler_file}"""						)
     # GradScaler state
     if scaler is not None:
        UpperCAmelCase_      :      Tuple      =					scaler.state_dict()
        UpperCAmelCase_      :      Dict      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        torch.save(__lowerCamelCase,	__lowerCamelCase						)
        logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""						)
     # Random number generator states
     UpperCAmelCase_      :      List[Any]      =					{}
     UpperCAmelCase_      :      List[str]      =					f"""{RNG_STATE_NAME}_{process_index}.pkl"""
     UpperCAmelCase_      :      Any      =					random.getstate()
     UpperCAmelCase_      :      Optional[Any]      =					np.random.get_state()
     UpperCAmelCase_      :      Dict      =					torch.get_rng_state()
     if is_xpu_available():
        UpperCAmelCase_      :      Optional[int]      =					torch.xpu.get_rng_state_all()
     else:
        UpperCAmelCase_      :      int      =					torch.cuda.get_rng_state_all()
     if is_tpu_available():
        UpperCAmelCase_      :      List[str]      =					xm.get_rng_state()
     UpperCAmelCase_      :      Union[str, Any]      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
     torch.save(__lowerCamelCase,	__lowerCamelCase						)
     logger.info(f"""Random states saved in {output_states_file}"""						)
     return output_dir
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase=None,	__lowerCamelCase=None,	**__lowerCamelCase,	):
     if map_location not in [None, "cpu", "on_device"]:
        raise TypeError(
            "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"						)
     if map_location is None:
        UpperCAmelCase_      :      int      =					"cpu"
     elif map_location == "on_device":
        UpperCAmelCase_      :      List[Any]      =					PartialState().device
     # Model states
     for i, model in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Optional[Any]      =					f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Any      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        models[i].load_state_dict(torch.load(__lowerCamelCase,	map_location=__lowerCamelCase						),	**__lowerCamelCase						)
     logger.info("All model weights loaded successfully"						)
  
     # Optimizer states
     for i, opt in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Tuple      =					f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Tuple      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        UpperCAmelCase_      :      Union[str, Any]      =					torch.load(__lowerCamelCase,	map_location=__lowerCamelCase						)
        optimizers[i].load_state_dict(__lowerCamelCase						)
     logger.info("All optimizer states loaded successfully"						)
  
     # Scheduler states
     for i, scheduler in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      List[str]      =					f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      str      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        scheduler.load_state_dict(torch.load(__lowerCamelCase						)						)
     logger.info("All scheduler states loaded successfully"						)
  
     # GradScaler state
     if scaler is not None:
        UpperCAmelCase_      :      str      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        scaler.load_state_dict(torch.load(__lowerCamelCase						)						)
        logger.info("GradScaler state loaded successfully"						)
  
     # Random states
     try:
        UpperCAmelCase_      :      Tuple      =					torch.load(os.path.join(__lowerCamelCase,	f"""{RNG_STATE_NAME}_{process_index}.pkl"""						)						)
        random.setstate(states["random_state"]						)
        np.random.set_state(states["numpy_random_seed"]						)
        torch.set_rng_state(states["torch_manual_seed"]						)
        if is_xpu_available():
           torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]						)
        else:
           torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]						)
        if is_tpu_available():
           xm.set_rng_state(states["xm_seed"]						)
        logger.info("All random states loaded successfully"						)
     except Exception:
        logger.info("Could not load random states"						)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = 0						):
     UpperCAmelCase_      :      Dict      =					Path(__lowerCamelCase						) / f"""custom_checkpoint_{index}.pkl"""
     logger.info(f"""Saving the state of {get_pretty_name(__lowerCamelCase						)} to {save_location}"""						)
     torch.save(obj.state_dict(),	__lowerCamelCase						)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = 0						):
     UpperCAmelCase_      :      Any      =					f"""{path}/custom_checkpoint_{index}.pkl"""
     logger.info(f"""Loading the state of {get_pretty_name(__lowerCamelCase						)} from {load_location}"""						)
     obj.load_state_dict(torch.load(__lowerCamelCase,	map_location="cpu"						)						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					parent
       UpperCAmelCase_      :      List[Any]      =					13
       UpperCAmelCase_      :      Dict      =					7
       UpperCAmelCase_      :      Optional[int]      =					True
       UpperCAmelCase_      :      List[Any]      =					True
       UpperCAmelCase_      :      Optional[int]      =					True
       UpperCAmelCase_      :      int      =					True
       UpperCAmelCase_      :      Any      =					True
       UpperCAmelCase_      :      Any      =					99
       UpperCAmelCase_      :      Dict      =					32
       UpperCAmelCase_      :      Dict      =					2
       UpperCAmelCase_      :      str      =					4
       UpperCAmelCase_      :      int      =					37
       UpperCAmelCase_      :      Union[str, Any]      =					"gelu"
       UpperCAmelCase_      :      List[str]      =					0.1
       UpperCAmelCase_      :      List[str]      =					0.1
       UpperCAmelCase_      :      int      =					512
       UpperCAmelCase_      :      str      =					16
       UpperCAmelCase_      :      Union[str, Any]      =					2
       UpperCAmelCase_      :      Dict      =					0.02
       UpperCAmelCase_      :      Tuple      =					3
       UpperCAmelCase_      :      Tuple      =					4
       UpperCAmelCase_      :      int      =					None
       UpperCAmelCase_      :      Dict      =					self.vocab_size - 1
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size						)
  
       UpperCAmelCase_      :      Optional[int]      =					None
       if self.use_input_mask:
          UpperCAmelCase_      :      Optional[Any]      =					random_attention_mask([self.batch_size, self.seq_length]						)
  
       UpperCAmelCase_      :      List[Any]      =					None
       if self.use_token_type_ids:
          UpperCAmelCase_      :      Optional[int]      =					ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size						)
  
       UpperCAmelCase_      :      str      =					None
       if self.use_mc_token_ids:
          UpperCAmelCase_      :      Any      =					ids_tensor([self.batch_size, self.num_choices]       ,     self.seq_length						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					None
       UpperCAmelCase_      :      str      =					None
       UpperCAmelCase_      :      str      =					None
       if self.use_labels:
          UpperCAmelCase_      :      List[Any]      =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size						)
          UpperCAmelCase_      :      str      =					ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels						)
          UpperCAmelCase_      :      Optional[Any]      =					ids_tensor([self.batch_size]       ,     self.num_choices						)
  
       UpperCAmelCase_      :      str      =					CTRLConfig(
           vocab_size=self.vocab_size       ,     n_embd=self.hidden_size       ,     n_layer=self.num_hidden_layers       ,     n_head=self.num_attention_heads       ,     n_positions=self.max_position_embeddings       ,     pad_token_id=self.pad_token_id       ,     )
  
       UpperCAmelCase_      :      Tuple      =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,     2						)
  
       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     *lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					TFCTRLModel(config=lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase_      :      Union[str, Any]      =					model(lowercase_						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					[input_ids, None, input_mask]  # None is the input for 'past'
       UpperCAmelCase_      :      str      =					model(lowercase_						)
  
       UpperCAmelCase_      :      str      =					model(lowercase_						)
  
       self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     *lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					TFCTRLLMHeadModel(config=lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase_      :      List[str]      =					model(lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     *lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					self.num_labels
       UpperCAmelCase_      :      str      =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size						)
       UpperCAmelCase_      :      Union[str, Any]      =					{
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       UpperCAmelCase_      :      Union[str, Any]      =					TFCTRLForSequenceClassification(lowercase_						)
       UpperCAmelCase_      :      Tuple      =					model(lowercase_						)
       self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					self.prepare_config_and_inputs()
  
       (
           (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     
       )      :      Tuple      =					config_and_inputs
  
       UpperCAmelCase_      :      int      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
  
  
  
  
  @require_tf
  class A_			(lowercase__       ,lowercase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Dict						  =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
    SCREAMING_SNAKE_CASE__    :		str						  =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
    SCREAMING_SNAKE_CASE__    :		Any						  =	(
        {
            """feature-extraction""": TFCTRLModel,
            """text-classification""": TFCTRLForSequenceClassification,
            """text-generation""": TFCTRLLMHeadModel,
            """zero-shot""": TFCTRLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    SCREAMING_SNAKE_CASE__    :		Dict						  =	False
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	False
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
          # config could not be created.
          return True
  
       return False
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					TFCTRLModelTester(self						)
       UpperCAmelCase_      :      Dict      =					ConfigTester(self       ,     config_class=lowercase_       ,     n_embd=37						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       self.config_tester.run_common_tests()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_model(*lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_lm_head(*lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_      :      Tuple      =					[TFCTRLLMHeadModel]
       UpperCAmelCase_      :      Dict      =					[TFCTRLForSequenceClassification]
  
       for model_class in self.all_model_classes:
          UpperCAmelCase_      :      Optional[int]      =					model_class(lowercase_						)
          model.build()  # may be needed for the get_bias() call below
          assert isinstance(model.get_input_embeddings()       ,     tf.keras.layers.Layer						)
  
          if model_class in list_lm_models:
             UpperCAmelCase_      :      List[str]      =					model.get_output_embeddings()
             assert isinstance(lowercase_       ,     tf.keras.layers.Layer						)
             UpperCAmelCase_      :      List[Any]      =					model.get_bias()
             assert isinstance(lowercase_       ,     lowercase_						)
             for k, v in name.items():
                assert isinstance(lowercase_       ,     tf.Variable						)
          elif model_class in list_other_models_with_output_ebd:
             UpperCAmelCase_      :      int      =					model.get_output_embeddings()
             assert isinstance(lowercase_       ,     tf.keras.layers.Layer						)
             UpperCAmelCase_      :      Any      =					model.get_bias()
             assert name is None
          else:
             UpperCAmelCase_      :      Dict      =					model.get_output_embeddings()
             assert x is None
             UpperCAmelCase_      :      Tuple      =					model.get_bias()
             assert name is None
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCAmelCase_      :      List[Any]      =					TFCTRLModel.from_pretrained(lowercase_						)
          self.assertIsNotNone(lowercase_						)
  
  
  
  
  @require_tf
  class A_			(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					TFCTRLLMHeadModel.from_pretrained("ctrl"						)
       UpperCAmelCase_      :      Union[str, Any]      =					tf.convert_to_tensor([[1_1859, 0, 1611, 8]]       ,     dtype=tf.intaa						)  # Legal the president is
       UpperCAmelCase_      :      int      =					[
           1_1859,
           0,
           1611,
           8,
           5,
           150,
           2_6449,
           2,
           19,
           348,
           469,
           3,
           2595,
           48,
           2_0740,
           24_6533,
           24_6533,
           19,
           30,
           5,
       ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
       UpperCAmelCase_      :      List[str]      =					model.generate(lowercase_       ,     do_sample=lowercase_						)
       self.assertListEqual(output_ids[0].numpy().tolist()       ,     lowercase_						)
  
  
  