

'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_CAUSAL_LM_MAPPING
				_a    	=     TF_MODEL_FOR_CAUSAL_LM_MAPPING



				@require_torch
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline(task='text-generation',     model='sshleifer/tiny-ctrl',     framework='pt'       )
											# Using `do_sample=False` to force deterministic output
											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test',     do_sample=snake_case__       )
											self.assertEqual(
											    snake_case__,     [
											        {
											            'generated_text': (
											                'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
											                ' oscope. FiliFili@@'
											            )
											        }
											    ],     )

											SCREAMING_SNAKE_CASE_				=				text_generator(['This is a test', 'This is a second test']       )
											self.assertEqual(
											    snake_case__,     [
											        [
											            {
											                'generated_text': (
											                    'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
											                    ' oscope. FiliFili@@'
											                )
											            }
											        ],
											        [
											            {
											                'generated_text': (
											                    'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
											                    ' oscope. oscope. FiliFili@@'
											                )
											            }
											        ],
											    ],     )

											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test',     do_sample=snake_case__,     num_return_sequences=2,     return_tensors=snake_case__       )
											self.assertEqual(
											    snake_case__,     [
											        {'generated_token_ids': ANY(snake_case__       )},
											        {'generated_token_ids': ANY(snake_case__       )},
											    ],     )
											SCREAMING_SNAKE_CASE_				=				text_generator.model.config.eos_token_id
											SCREAMING_SNAKE_CASE_				=				'<pad>'
											SCREAMING_SNAKE_CASE_				=				text_generator(
											    ['This is a test', 'This is a second test'],     do_sample=snake_case__,     num_return_sequences=2,     batch_size=2,     return_tensors=snake_case__,     )
											self.assertEqual(
											    snake_case__,     [
											        [
											            {'generated_token_ids': ANY(snake_case__       )},
											            {'generated_token_ids': ANY(snake_case__       )},
											        ],
											        [
											            {'generated_token_ids': ANY(snake_case__       )},
											            {'generated_token_ids': ANY(snake_case__       )},
											        ],
											    ],     )



				@require_tf
				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				pipeline(task='text-generation',     model='sshleifer/tiny-ctrl',     framework='tf'       )

											# Using `do_sample=False` to force deterministic output
											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test',     do_sample=snake_case__       )
											self.assertEqual(
											    snake_case__,     [
											        {
											            'generated_text': (
											                'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
											                ' please,'
											            )
											        }
											    ],     )

											SCREAMING_SNAKE_CASE_				=				text_generator(['This is a test', 'This is a second test'],     do_sample=snake_case__       )
											self.assertEqual(
											    snake_case__,     [
											        [
											            {
											                'generated_text': (
											                    'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
											                    ' please,'
											                )
											            }
											        ],
											        [
											            {
											                'generated_text': (
											                    'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
											                    ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
											                )
											            }
											        ],
											    ],     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				TextGenerationPipeline(model=snake_case__,     tokenizer=snake_case__       )
											return text_generator, ["This is a test", "Another test"]



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				'Hello I believe in'
											SCREAMING_SNAKE_CASE_				=				pipeline('text-generation',     model='hf-internal-testing/tiny-random-gpt2'       )
											SCREAMING_SNAKE_CASE_				=				text_generator(snake_case__       )
											self.assertEqual(
											    snake_case__,     [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}],     )

											SCREAMING_SNAKE_CASE_				=				text_generator(snake_case__,     stop_sequence=' fe'       )
											self.assertEqual(snake_case__,     [{'generated_text': 'Hello I believe in fe'}]       )



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				text_generator.model
											SCREAMING_SNAKE_CASE_				=				text_generator.tokenizer

											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test'       )
											self.assertEqual(snake_case__,     [{'generated_text': ANY(snake_case__       )}]       )
											self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'       )       )

											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test',     return_full_text=snake_case__       )
											self.assertEqual(snake_case__,     [{'generated_text': ANY(snake_case__       )}]       )
											self.assertNotIn('This is a test',     outputs[0]['generated_text']       )

											SCREAMING_SNAKE_CASE_				=				pipeline(task='text-generation',     model=snake_case__,     tokenizer=snake_case__,     return_full_text=snake_case__       )
											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test'       )
											self.assertEqual(snake_case__,     [{'generated_text': ANY(snake_case__       )}]       )
											self.assertNotIn('This is a test',     outputs[0]['generated_text']       )

											SCREAMING_SNAKE_CASE_				=				text_generator('This is a test',     return_full_text=snake_case__       )
											self.assertEqual(snake_case__,     [{'generated_text': ANY(snake_case__       )}]       )
											self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'       )       )

											SCREAMING_SNAKE_CASE_				=				text_generator(['This is great !', 'Something else'],     num_return_sequences=2,     do_sample=snake_case__       )
											self.assertEqual(
											    snake_case__,     [
											        [{'generated_text': ANY(snake_case__       )}, {'generated_text': ANY(snake_case__       )}],
											        [{'generated_text': ANY(snake_case__       )}, {'generated_text': ANY(snake_case__       )}],
											    ],     )

											if text_generator.tokenizer.pad_token is not None:
																		SCREAMING_SNAKE_CASE_				=				text_generator(
																		    ['This is great !', 'Something else'],     num_return_sequences=2,     batch_size=2,     do_sample=snake_case__       )
																		self.assertEqual(
																		    snake_case__,     [
																		        [{'generated_text': ANY(snake_case__       )}, {'generated_text': ANY(snake_case__       )}],
																		        [{'generated_text': ANY(snake_case__       )}, {'generated_text': ANY(snake_case__       )}],
																		    ],     )

											with self.assertRaises(snake_case__       ):
																		SCREAMING_SNAKE_CASE_				=				text_generator('test',     return_full_text=snake_case__,     return_text=snake_case__       )
											with self.assertRaises(snake_case__       ):
																		SCREAMING_SNAKE_CASE_				=				text_generator('test',     return_full_text=snake_case__,     return_tensors=snake_case__       )
											with self.assertRaises(snake_case__       ):
																		SCREAMING_SNAKE_CASE_				=				text_generator('test',     return_text=snake_case__,     return_tensors=snake_case__       )

											# Empty prompt is slighly special
											# it requires BOS token to exist.
											# Special case for Pegasus which will always append EOS so will
											# work even without BOS.
											if (
											    text_generator.tokenizer.bos_token_id is not None
											    or "Pegasus" in tokenizer.__class__.__name__
											    or "Git" in model.__class__.__name__
											):
																		SCREAMING_SNAKE_CASE_				=				text_generator(''       )
																		self.assertEqual(snake_case__,     [{'generated_text': ANY(snake_case__       )}]       )
											else:
																		with self.assertRaises((ValueError, AssertionError)       ):
																									SCREAMING_SNAKE_CASE_				=				text_generator(''       )

											if text_generator.framework == "tf":
																		# TF generation does not support max_new_tokens, and it's impossible
																		# to control long generation with only max_length without
																		# fancy calculation, dismissing tests for now.
																		return
											# We don't care about infinite range models.
											# They already work.
											# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
											SCREAMING_SNAKE_CASE_				=				['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
											if (
											    tokenizer.model_max_length < 10000
											    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
											):
																		# Handling of large generations
																		with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)       ):
																									text_generator('This is a test' * 500,     max_new_tokens=20       )

																		SCREAMING_SNAKE_CASE_				=				text_generator('This is a test' * 500,     handle_long_generation='hole',     max_new_tokens=20       )
																		# Hole strategy cannot work
																		with self.assertRaises(snake_case__       ):
																									text_generator(
																									    'This is a test' * 500,     handle_long_generation='hole',     max_new_tokens=tokenizer.model_max_length + 10,     )



				@require_torch
				@require_accelerate
				@require_torch_gpu
				def   a__					(    self       )     ->							str:
											import torch

											# Classic `model_kwargs`
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    model='hf-internal-testing/tiny-random-bloom',     model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa},     )
											self.assertEqual(pipe.model.device,     torch.device(0       )       )
											self.assertEqual(pipe.model.lm_head.weight.dtype,     torch.bfloataa       )
											SCREAMING_SNAKE_CASE_				=				pipe('This is a test'       )
											self.assertEqual(
											    snake_case__,     [
											        {
											            'generated_text': (
											                'This is a test test test test test test test test test test test test test test test test'
											                ' test'
											            )
											        }
											    ],     )

											# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
											SCREAMING_SNAKE_CASE_				=				pipeline(model='hf-internal-testing/tiny-random-bloom',     device_map='auto',     torch_dtype=torch.bfloataa       )
											self.assertEqual(pipe.model.device,     torch.device(0       )       )
											self.assertEqual(pipe.model.lm_head.weight.dtype,     torch.bfloataa       )
											SCREAMING_SNAKE_CASE_				=				pipe('This is a test'       )
											self.assertEqual(
											    snake_case__,     [
											        {
											            'generated_text': (
											                'This is a test test test test test test test test test test test test test test test test'
											                ' test'
											            )
											        }
											    ],     )

											# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
											SCREAMING_SNAKE_CASE_				=				pipeline(model='hf-internal-testing/tiny-random-bloom',     device_map='auto'       )
											self.assertEqual(pipe.model.device,     torch.device(0       )       )
											self.assertEqual(pipe.model.lm_head.weight.dtype,     torch.floataa       )
											SCREAMING_SNAKE_CASE_				=				pipe('This is a test'       )
											self.assertEqual(
											    snake_case__,     [
											        {
											            'generated_text': (
											                'This is a test test test test test test test test test test test test test test test test'
											                ' test'
											            )
											        }
											    ],     )



				@require_torch
				@require_torch_gpu
				def   a__					(    self       )     ->							Any:
											import torch

											SCREAMING_SNAKE_CASE_				=				pipeline(model='hf-internal-testing/tiny-random-bloom',     device=0,     torch_dtype=torch.floataa       )
											pipe('This is a test'       )



				@require_torch
				@require_accelerate
				@require_torch_gpu
				def   a__					(    self       )     ->							Dict:
											import torch

											SCREAMING_SNAKE_CASE_				=				pipeline(model='hf-internal-testing/tiny-random-bloom',     device_map='auto',     torch_dtype=torch.floataa       )
											pipe('This is a test',     do_sample=snake_case__,     top_p=0.5       )





				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				'Hello world'
											SCREAMING_SNAKE_CASE_				=				pipeline('text-generation',     model='hf-internal-testing/tiny-random-gpt2'       )
											if text_generator.model.framework == "tf":
																		SCREAMING_SNAKE_CASE_				=				logging.get_logger('transformers.generation.tf_utils'       )
											else:
																		SCREAMING_SNAKE_CASE_				=				logging.get_logger('transformers.generation.utils'       )
											SCREAMING_SNAKE_CASE_				=				'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

											# Both are set by the user -> log warning
											with CaptureLogger(snake_case__       ) as cl:
																		SCREAMING_SNAKE_CASE_				=				text_generator(snake_case__,     max_length=10,     max_new_tokens=1       )
											self.assertIn(snake_case__,     cl.out       )

											# The user only sets one -> no warning
											with CaptureLogger(snake_case__       ) as cl:
																		SCREAMING_SNAKE_CASE_				=				text_generator(snake_case__,     max_new_tokens=1       )
											self.assertNotIn(snake_case__,     cl.out       )

											with CaptureLogger(snake_case__       ) as cl:
																		SCREAMING_SNAKE_CASE_				=				text_generator(snake_case__,     max_length=10       )
											self.assertNotIn(snake_case__,     cl.out       )









'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
       import tensorflow as tf

       from transformers.models.xglm.modeling_tf_xglm import (
           TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXGLMForCausalLM,
           TFXGLMModel,
       )
@require_tf
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     XGLMConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=14   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=0.02   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							parent
            _lowerCAmelCase     :				Tuple       =							batch_size
            _lowerCAmelCase     :				Optional[Any]       =							seq_length
            _lowerCAmelCase     :				Dict       =							is_training
            _lowerCAmelCase     :				str       =							use_input_mask
            _lowerCAmelCase     :				Tuple       =							use_labels
            _lowerCAmelCase     :				Union[str, Any]       =							vocab_size
            _lowerCAmelCase     :				Any       =							d_model
            _lowerCAmelCase     :				Dict       =							num_hidden_layers
            _lowerCAmelCase     :				Optional[Any]       =							num_attention_heads
            _lowerCAmelCase     :				Tuple       =							ffn_dim
            _lowerCAmelCase     :				Tuple       =							activation_function
            _lowerCAmelCase     :				int       =							activation_dropout
            _lowerCAmelCase     :				Optional[Any]       =							attention_dropout
            _lowerCAmelCase     :				Union[str, Any]       =							max_position_embeddings
            _lowerCAmelCase     :				List[Any]       =							initializer_range
            _lowerCAmelCase     :				List[str]       =							None
            _lowerCAmelCase     :				Any       =							0
            _lowerCAmelCase     :				Any       =							2
            _lowerCAmelCase     :				Optional[Any]       =							1







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig.from_pretrained('facebook/xglm-564M' )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							tf.clip_by_value(
                ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )   ,						clip_value_min=0   ,						clip_value_max=3 )

            _lowerCAmelCase     :				str       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				int       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				str       =							self.get_config()

            _lowerCAmelCase     :				Union[str, Any]       =							floats_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2 )

            return (
                config,
                input_ids,
                input_mask,
                head_mask,
            )







     def a						(			self ):

            '''simple docstring'''

            return XGLMConfig(
                vocab_size=self.vocab_size   ,						d_model=self.hidden_size   ,						num_layers=self.num_hidden_layers   ,						attention_heads=self.num_attention_heads   ,						ffn_dim=self.ffn_dim   ,						activation_function=self.activation_function   ,						activation_dropout=self.activation_dropout   ,						attention_dropout=self.attention_dropout   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						use_cache=snake_case__   ,						bos_token_id=self.bos_token_id   ,						eos_token_id=self.eos_token_id   ,						pad_token_id=self.pad_token_id   ,						return_dict=snake_case__   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.prepare_config_and_inputs()

            (
                (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  (
                _lowerCAmelCase
            )      ,  
            )     :				Dict       =							config_and_inputs

            _lowerCAmelCase     :				Optional[int]       =							{
                'input_ids': input_ids,
                'head_mask': head_mask,
            }

            return config, inputs_dict
@require_tf
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     __magic_name__           =     (TFXGLMForCausalLM,) if is_tf_available() else ()
     __magic_name__           =     (
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							TFXGLMModelTester(self )
            _lowerCAmelCase     :				Dict       =							ConfigTester(self   ,						config_class=snake_case__   ,						n_embd=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Optional[Any]       =							TFXGLMModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
     def a						(			self ):

            '''simple docstring'''

            super().test_resize_token_embeddings()
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				str       =							tf.convert_to_tensor([[2, 268, 9865]]   ,						dtype=tf.intaa )  # The dog
            # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
            # fmt: off
            _lowerCAmelCase     :				str       =							[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
            # fmt: on
            _lowerCAmelCase     :				Optional[int]       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						num_beams=1 )
            if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist()   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

            tf.random.set_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							tokenizer('Today is a nice day and'   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Dict       =							tokenized.input_ids
            # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
            with tf.device(':/CPU:0' ):
                   _lowerCAmelCase     :				Dict       =							model.generate(snake_case__   ,						do_sample=snake_case__   ,						seed=[7, 0] )
            _lowerCAmelCase     :				List[Any]       =							tokenizer.decode(output_ids[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							(
                'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
            )
            self.assertEqual(snake_case__   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
            _lowerCAmelCase     :				int       =							XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

            _lowerCAmelCase     :				Tuple       =							'left'

            # use different length sentences to test batching
            _lowerCAmelCase     :				Optional[int]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When',
                'Hello, my dog is a little',
            ]

            _lowerCAmelCase     :				Tuple       =							tokenizer(snake_case__   ,						return_tensors='tf'   ,						padding=snake_case__ )
            _lowerCAmelCase     :				Any       =							inputs['input_ids']

            _lowerCAmelCase     :				Any       =							model.generate(input_ids=snake_case__   ,						attention_mask=inputs['attention_mask']   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Dict       =							tokenizer(sentences[0]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				int       =							tokenizer(sentences[1]   ,						return_tensors='tf' ).input_ids
            _lowerCAmelCase     :				Optional[Any]       =							model.generate(input_ids=snake_case__   ,						max_new_tokens=12 )

            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.batch_decode(snake_case__   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Any       =							tokenizer.decode(output_non_padded[0]   ,						skip_special_tokens=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							tokenizer.decode(output_padded[0]   ,						skip_special_tokens=snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							[
                'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                'left-padding, such as in batched generation. The output for the sequence below should be the same '
                'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                'a single',
                'Hello, my dog is a little bit of a shy one, but he is very friendly',
            ]
            self.assertListEqual(snake_case__   ,						snake_case__ )
            self.assertListEqual(snake_case__   ,						[non_padded_sentence, padded_sentence] )

