





'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A__				:   Tuple      =logging.get_logger(__name__)

A__				:   List[Any]      ={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
A__				:   List[Any]      =[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      for attribute in key.split("""."""  ):
            _lowerCAmelCase							     =							getattr(lowerCAmelCase							,	lowerCAmelCase  )

      if weight_type is not None:
            _lowerCAmelCase							     =							getattr(lowerCAmelCase							,	lowerCAmelCase  ).shape
      else:
            _lowerCAmelCase							     =							hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                f" {value.shape} for {full_name}"  )

      if weight_type == "weight":
            _lowerCAmelCase							     =							value
      elif weight_type == "weight_g":
            _lowerCAmelCase							     =							value
      elif weight_type == "weight_v":
            _lowerCAmelCase							     =							value
      elif weight_type == "bias":
            _lowerCAmelCase							     =							value
      elif weight_type == "running_mean":
            _lowerCAmelCase							     =							value
      elif weight_type == "running_var":
            _lowerCAmelCase							     =							value
      elif weight_type == "num_batches_tracked":
            _lowerCAmelCase							     =							value
      elif weight_type == "inv_freq":
            _lowerCAmelCase							     =							value
      else:
            _lowerCAmelCase							     =							value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."  )
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							fairseq_model.state_dict()

      _lowerCAmelCase							     =							hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCAmelCase							     =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	hf_model.config.feat_extract_norm == """group"""							,	)
                  _lowerCAmelCase							     =							True
            else:
                  for key, mapped_key in MAPPING.items():
                        _lowerCAmelCase							     =							"""wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
                              _lowerCAmelCase							     =							True
                              if "*" in mapped_key:
                                    _lowerCAmelCase							     =							name.split(lowerCAmelCase  )[0].split("""."""  )[-2]
                                    _lowerCAmelCase							     =							mapped_key.replace("""*"""							,	lowerCAmelCase  )
                              if "pos_bias_u" in name:
                                    _lowerCAmelCase							     =							None
                              elif "pos_bias_v" in name:
                                    _lowerCAmelCase							     =							None
                              elif "weight_g" in name:
                                    _lowerCAmelCase							     =							"""weight_g"""
                              elif "weight_v" in name:
                                    _lowerCAmelCase							     =							"""weight_v"""
                              elif "bias" in name:
                                    _lowerCAmelCase							     =							"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCAmelCase							     =							"""weight"""
                              elif "running_mean" in name:
                                    _lowerCAmelCase							     =							"""running_mean"""
                              elif "inv_freq" in name:
                                    _lowerCAmelCase							     =							"""inv_freq"""
                              elif "running_var" in name:
                                    _lowerCAmelCase							     =							"""running_var"""
                              elif "num_batches_tracked" in name:
                                    _lowerCAmelCase							     =							"""num_batches_tracked"""
                              else:
                                    _lowerCAmelCase							     =							None
                              set_recursively(lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  )
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase  )

      logger.warning(f"Unused weights: {unused_weights}"  )
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							full_name.split("""conv_layers."""  )[-1]
      _lowerCAmelCase							     =							name.split("""."""  )
      _lowerCAmelCase							     =							int(items[0]  )
      _lowerCAmelCase							     =							int(items[1]  )

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f"{full_name} has size {value.shape}, but"
                            f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."  )
                  _lowerCAmelCase							     =							value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."  )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f"{full_name} has size {value.shape}, but"
                            f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."  )
                  _lowerCAmelCase							     =							value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."  )
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"{full_name} has size {value.shape}, but"
                            f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."  )
                  _lowerCAmelCase							     =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f"{full_name} has size {value.shape}, but"
                            f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."  )
                  _lowerCAmelCase							     =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
      else:
            unused_weights.append(lowerCAmelCase  )
@torch.no_grad()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase=None							,	lowerCAmelCase=None							,	lowerCAmelCase=True  ):





      """simple docstring"""


      if config_path is not None:
            _lowerCAmelCase							     =							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase							,	hidden_act="""swish"""  )
      else:
            _lowerCAmelCase							     =							WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            _lowerCAmelCase							     =							"""rotary"""

      if is_finetuned:
            if dict_path:
                  _lowerCAmelCase							     =							Dictionary.load(lowerCAmelCase  )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _lowerCAmelCase							     =							target_dict.pad_index
                  _lowerCAmelCase							     =							target_dict.bos_index
                  _lowerCAmelCase							     =							target_dict.eos_index
                  _lowerCAmelCase							     =							len(target_dict.symbols  )
                  _lowerCAmelCase							     =							os.path.join(lowerCAmelCase							,	"""vocab.json"""  )
                  if not os.path.isdir(lowerCAmelCase  ):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase  )  )
                        return
                  os.makedirs(lowerCAmelCase							,	exist_ok=lowerCAmelCase  )
                  _lowerCAmelCase							     =							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCAmelCase							     =							0
                  _lowerCAmelCase							     =							1
                  with open(lowerCAmelCase							,	"""w"""							,	encoding="""utf-8"""  ) as vocab_handle:
                        json.dump(lowerCAmelCase							,	lowerCAmelCase  )
                  _lowerCAmelCase							     =							WavaVecaCTCTokenizer(
                      lowerCAmelCase							,	unk_token=target_dict.unk_word							,	pad_token=target_dict.pad_word							,	bos_token=target_dict.bos_word							,	eos_token=target_dict.eos_word							,	word_delimiter_token="""|"""							,	do_lower_case=lowerCAmelCase							,	)
                  _lowerCAmelCase							     =							True if config.feat_extract_norm == """layer""" else False
                  _lowerCAmelCase							     =							WavaVecaFeatureExtractor(
                      feature_size=1							,	sampling_rate=1_60_00							,	padding_value=0							,	do_normalize=lowerCAmelCase							,	return_attention_mask=lowerCAmelCase							,	)
                  _lowerCAmelCase							     =							WavaVecaProcessor(feature_extractor=lowerCAmelCase							,	tokenizer=lowerCAmelCase  )
                  processor.save_pretrained(lowerCAmelCase  )

            _lowerCAmelCase							     =							WavaVecaConformerForCTC(lowerCAmelCase  )
      else:
            _lowerCAmelCase							     =							WavaVecaConformerForPreTraining(lowerCAmelCase  )

      if is_finetuned:
            _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
      else:
            _lowerCAmelCase							     =							argparse.Namespace(task="""audio_pretraining"""  )
            _lowerCAmelCase							     =							fairseq.tasks.setup_task(lowerCAmelCase  )

            _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,	task=lowerCAmelCase  )

      _lowerCAmelCase							     =							model[0].eval()

      recursively_load_weights(lowerCAmelCase							,	lowerCAmelCase							,	not is_finetuned  )

      hf_wavavec.save_pretrained(lowerCAmelCase  )


if __name__ == "__main__":
      A__				:   str      =argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      parser.add_argument(
          '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
      )
      A__				:   List[Any]      =parser.parse_args()
      convert_wavaveca_conformer_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )













import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)

class 	A_       :
	def __init__(    self							,					_A							,					_A=1_4							,					_A=7							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=9_9							,					_A=3_2							,					_A=5							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=5_1_2							,					_A=1_6							,					_A=2							,					_A=0.02							,					_A=3							,					_A=4							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_token_type_ids
				UpperCAmelCase   		=				use_input_mask
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				use_mc_token_ids
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				type_sequence_label_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				num_labels
				UpperCAmelCase   		=				num_choices
				UpperCAmelCase   		=				scope
				UpperCAmelCase   		=				self.vocab_size - 1
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size		)

				UpperCAmelCase   		=				None
				if self.use_input_mask:
							UpperCAmelCase   		=				random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase   		=				None
				if self.use_token_type_ids:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.type_vocab_size		)

				UpperCAmelCase   		=				None
				if self.use_mc_token_ids:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.num_choices]							,					self.seq_length		)

				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.num_choices		)

				UpperCAmelCase   		=				self.get_config()

				UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







				return 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							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModel(config=_A		)
				model.to(_A		)
				model.eval()

				model(_A							,					token_type_ids=_A							,					head_mask=_A		)
				model(_A							,					token_type_ids=_A		)
				UpperCAmelCase   		=				model(_A		)
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)		)
				self.parent.assertEqual(len(result.past_key_values		)							,					config.n_layer		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel(_A		)
				model.to(_A		)
				model.eval()

				UpperCAmelCase   		=				model(_A							,					token_type_ids=_A							,					labels=_A		)
				self.parent.assertEqual(result.loss.shape							,					()		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()

				(
				    (
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			
				)   		=				config_and_inputs

				UpperCAmelCase   		=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict







	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.num_labels
				UpperCAmelCase   		=				CTRLForSequenceClassification(_A		)
				model.to(_A		)
				model.eval()
				UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
				UpperCAmelCase   		=				model(_A							,					token_type_ids=_A							,					labels=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)		)

@require_torch
class 	A_       (a_     ,							a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	UpperCAmelCase__					=			(CTRLLMHeadModel,) if is_torch_available() else ()
	UpperCAmelCase__					=			(
	    {
	        '''feature-extraction''': CTRLModel,
	        '''text-classification''': CTRLForSequenceClassification,
	        '''text-generation''': CTRLLMHeadModel,
	        '''zero-shot''': CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					n_embd=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_A		)
	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase   		=				CTRLModel.from_pretrained(_A		)
							self.assertIsNotNone(_A		)







	@unittest.skip('''The model doesn\'t support left padding'''		)  # and it's not used enough to be worth fixing :)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass

@require_torch
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel.from_pretrained('''ctrl'''		)
				model.to(_A		)
				UpperCAmelCase   		=				torch.tensor(
				    [[1_1_8_5_9, 0, 1_6_1_1, 8]]							,					dtype=torch.long							,					device=_A		)  # Legal the president is
				UpperCAmelCase   		=				[
				    1_1_8_5_9,
				    0,
				    1_6_1_1,
				    8,
				    5,
				    1_5_0,
				    2_6_4_4_9,
				    2,
				    1_9,
				    3_4_8,
				    4_6_9,
				    3,
				    2_5_9_5,
				    4_8,
				    2_0_7_4_0,
				    2_4_6_5_3_3,
				    2_4_6_5_3_3,
				    1_9,
				    3_0,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				UpperCAmelCase   		=				model.generate(_A							,					do_sample=_A		)
				self.assertListEqual(output_ids[0].tolist()							,					_A		)



