




"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
			import botoa  # noqa: F401







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       botoa.client('''iam'''	)

			__lowercase			:  Union[str, Any]								=       {
			    '''Version''': '''2012-10-17''',
			    '''Statement''': [
			        {'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
			    ],
			}
			try:
						# create the role, associated with the chosen trust policy
						iam_client.create_role(
						    RoleName=__UpperCamelCase	,				AssumeRolePolicyDocument=json.dumps(__UpperCamelCase	,				indent=2	)	)
						__lowercase			:  Union[str, Any]								=       {
						    '''Version''': '''2012-10-17''',
						    '''Statement''': [
						        {
						            '''Effect''': '''Allow''',
						            '''Action''': [
						                '''sagemaker:*''',
						                '''ecr:GetDownloadUrlForLayer''',
						                '''ecr:BatchGetImage''',
						                '''ecr:BatchCheckLayerAvailability''',
						                '''ecr:GetAuthorizationToken''',
						                '''cloudwatch:PutMetricData''',
						                '''cloudwatch:GetMetricData''',
						                '''cloudwatch:GetMetricStatistics''',
						                '''cloudwatch:ListMetrics''',
						                '''logs:CreateLogGroup''',
						                '''logs:CreateLogStream''',
						                '''logs:DescribeLogStreams''',
						                '''logs:PutLogEvents''',
						                '''logs:GetLogEvents''',
						                '''s3:CreateBucket''',
						                '''s3:ListBucket''',
						                '''s3:GetBucketLocation''',
						                '''s3:GetObject''',
						                '''s3:PutObject''',
						            ],
						            '''Resource''': '''*''',
						        }
						    ],
						}
						# attach policy to role
						iam_client.put_role_policy(
						    RoleName=__UpperCamelCase	,				PolicyName=f"""{role_name}_policy_permission"""	,				PolicyDocument=json.dumps(__UpperCamelCase	,				indent=2	)	,				)
			except iam_client.exceptions.EntityAlreadyExistsException:
						print(f"""role {role_name} already exists. Using existing one"""	)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  str								=       botoa.client('''iam'''	)
			return iam_client.get_role(RoleName=__UpperCamelCase	)["Role"]["Arn"]







def      __UpperCAmelCase     ( ):
			__lowercase			:  int								=       _ask_options(
			    '''How do you want to authorize?'''	,				['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']	,				__UpperCamelCase	,				)
			__lowercase			:  Tuple								=       None
			if credentials_configuration == 0:
						__lowercase			:  List[Any]								=       _ask_field('''Enter your AWS Profile name: [default] '''	,				default='''default'''	)
						__lowercase			:  Any								=       aws_profile
			else:
						print(
						    '''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
						    '''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'''	)
						__lowercase			:  int								=       _ask_field('''AWS Access Key ID: '''	)
						__lowercase			:  Any								=       aws_access_key_id

						__lowercase			:  int								=       _ask_field('''AWS Secret Access Key: '''	)
						__lowercase			:  List[str]								=       aws_secret_access_key

			__lowercase			:  Union[str, Any]								=       _ask_field('''Enter your AWS Region: [us-east-1]'''	,				default='''us-east-1'''	)
			__lowercase			:  str								=       aws_region

			__lowercase			:  List[Any]								=       _ask_options(
			    '''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'''	,				['''Provide IAM Role name''', '''Create new IAM role using credentials''']	,				__UpperCamelCase	,				)
			if role_management == 0:
						__lowercase			:  List[Any]								=       _ask_field('''Enter your IAM role name: '''	)
			else:
						__lowercase			:  Dict								=       '''accelerate_sagemaker_execution_role'''
						print(f"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""	)
						_create_iam_role_for_sagemaker(__UpperCamelCase	)

			__lowercase			:  str								=       _ask_field(
			    '''Do you want to use custom Docker image? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  List[Any]								=       None
			if is_custom_docker_image:
						__lowercase			:  Dict								=       _ask_field('''Enter your Docker image: '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	)

			__lowercase			:  Optional[int]								=       _ask_field(
			    '''Do you want to provide SageMaker input channels with data locations? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  str								=       None
			if is_sagemaker_inputs_enabled:
						__lowercase			:  Any								=       _ask_field(
						    '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				)

			__lowercase			:  List[str]								=       _ask_field(
			    '''Do you want to enable SageMaker metrics? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  Optional[int]								=       None
			if is_sagemaker_metrics_enabled:
						__lowercase			:  Dict								=       _ask_field(
						    '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				)

			__lowercase			:  int								=       _ask_options(
			    '''What is the distributed mode?'''	,				['''No distributed training''', '''Data parallelism''']	,				_convert_sagemaker_distributed_mode	,				)
			__lowercase			:  Optional[int]								=       {}
			__lowercase			:  Dict								=       _ask_field(
			    '''Do you wish to optimize your script with torch dynamo?[yes/NO]:'''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			if use_dynamo:
						__lowercase			:  Tuple								=       '''dynamo_'''
						__lowercase			:  Dict								=       _ask_options(
						    '''Which dynamo backend would you like to use?'''	,				[x.lower() for x in DYNAMO_BACKENDS]	,				_convert_dynamo_backend	,				default=2	,				)
						__lowercase			:  List[Any]								=       _ask_field(
						    '''Do you want to customize the defaults sent to torch.compile? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)

						if use_custom_options:
									__lowercase			:  Optional[int]								=       _ask_options(
									    '''Which mode do you want to use?'''	,				__UpperCamelCase	,				lambda __UpperCamelCase	: TORCH_DYNAMO_MODES[int(__UpperCamelCase	)]	,				default='''default'''	,				)
									__lowercase			:  str								=       _ask_field(
									    '''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
									__lowercase			:  Union[str, Any]								=       _ask_field(
									    '''Do you want to enable dynamic shape tracing? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  str								=       '''Which EC2 instance type you want to use for your training?'''
			if distributed_type != SageMakerDistributedType.NO:
						__lowercase			:  Union[str, Any]								=       _ask_options(
						    __UpperCamelCase	,				__UpperCamelCase	,				lambda __UpperCamelCase	: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__UpperCamelCase	)]	)
			else:
						eca_instance_query += "? [ml.p3.2xlarge]:"
						__lowercase			:  Tuple								=       _ask_field(__UpperCamelCase	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				default='''ml.p3.2xlarge'''	)

			__lowercase			:  int								=       1
			if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
						__lowercase			:  Optional[Any]								=       _ask_field(
						    '''How many machines do you want use? [1]: '''	,				__UpperCamelCase	,				default=1	,				)

			__lowercase			:  List[Any]								=       _ask_options(
			    '''Do you wish to use FP16 or BF16 (mixed precision)?'''	,				['''no''', '''fp16''', '''bf16''', '''fp8''']	,				_convert_mixed_precision	,				)

			if use_dynamo and mixed_precision == "no":
						print(
						    '''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'''	)

			return SageMakerConfig(
			    image_uri=__UpperCamelCase	,				compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER	,				distributed_type=__UpperCamelCase	,				use_cpu=__UpperCamelCase	,				dynamo_config=__UpperCamelCase	,				eca_instance_type=__UpperCamelCase	,				profile=__UpperCamelCase	,				region=__UpperCamelCase	,				iam_role_name=__UpperCamelCase	,				mixed_precision=__UpperCamelCase	,				num_machines=__UpperCamelCase	,				sagemaker_inputs_file=__UpperCamelCase	,				sagemaker_metrics_file=__UpperCamelCase	,				)










'''simple docstring'''

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					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Tuple=14					,lowerCamelCase					:      Optional[Any]=7					,lowerCamelCase					:      str=True					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      Dict=True					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Dict=99					,lowerCamelCase					:      Dict=32					,lowerCamelCase					:      Optional[Any]=5					,lowerCamelCase					:      Tuple=4					,lowerCamelCase					:      Optional[int]=37					,lowerCamelCase					:      Optional[int]="gelu"					,lowerCamelCase					:      Optional[Any]=0.1					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Dict=512					,lowerCamelCase					:      int=16					,lowerCamelCase					:      Union[str, Any]=2					,lowerCamelCase					:      Tuple=0.02					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=4					,lowerCamelCase					:      Any=None					,):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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

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

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

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

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

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      __SCREAMING_SNAKE_CASE   				=							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					:      int     ):

      '''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   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      str					,*lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      model(lowerCamelCase					,token_type_ids=lowerCamelCase					,head_mask=lowerCamelCase     )
      model(lowerCamelCase					,token_type_ids=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      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   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      int					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,*lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,token_type_ids=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.loss.shape					,()     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()

      (
          (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  
      )   				=							config_and_inputs

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

      return config, inputs_dict





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      int					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,*lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

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







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			str						   =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			Dict						   =						(CTRLLMHeadModel,) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						True
   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      str					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict     ):

      '''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					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,n_embd=37     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*lowerCamelCase     )





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      pass





   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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





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

      '''simple docstring'''

      pass







@require_torch
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

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





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel.from_pretrained("""ctrl"""     )
      model.to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[1_1859, 0, 1611, 8]]					,dtype=torch.long					,device=lowerCamelCase     )  # Legal the president is
      __SCREAMING_SNAKE_CASE   				=							[
          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

      __SCREAMING_SNAKE_CASE   				=							model.generate(lowerCamelCase					,do_sample=lowerCamelCase     )
      self.assertListEqual(output_ids[0].tolist()					,lowerCamelCase     )





