



"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''gpt_neox'''






							def __init__(  self	,       snake_case=50432	,       snake_case=6144	,       snake_case=44	,       snake_case=64	,       snake_case=24576	,       snake_case="gelu"	,       snake_case=0.25	,       snake_case=10000	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case=2048	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=True	,       snake_case=0	,       snake_case=2	,       snake_case=False	,       snake_case=True	,       snake_case=None	,       **snake_case	,       )			->							Union[str, Any]:
									super().__init__(bos_token_id=snake_case	,       eos_token_id=snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		max_position_embeddings
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		rotary_pct
									_UpperCAmelCase							 =		rotary_emb_base
									_UpperCAmelCase							 =		attention_dropout
									_UpperCAmelCase							 =		hidden_dropout
									_UpperCAmelCase							 =		classifier_dropout
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		use_cache
									_UpperCAmelCase							 =		tie_word_embeddings
									_UpperCAmelCase							 =		use_parallel_residual
									_UpperCAmelCase							 =		rope_scaling
									self._rope_scaling_validation()

									if self.hidden_size % self.num_attention_heads != 0:
											raise ValueError(
											    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									if self.rope_scaling is None:
											return

									if not isinstance(self.rope_scaling	,       snake_case						) or len(self.rope_scaling						) != 2:
											raise ValueError(
											    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
											    f'got {self.rope_scaling}'						)
									_UpperCAmelCase							 =		self.rope_scaling.get('type'	,       snake_case						)
									_UpperCAmelCase							 =		self.rope_scaling.get('factor'	,       snake_case						)
									if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
											raise ValueError(
											    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'						)
									if rope_scaling_factor is None or not isinstance(snake_case	,       snake_case						) or rope_scaling_factor <= 1.0:
											raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'						)







"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch




@is_pipeline_test
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							_UpperCAmelCase      				=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		TextaTextGenerationPipeline(model=snake_case	,       tokenizer=snake_case						)
									return generator, ["Something to write", "Something else"]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		generator('Something there'						)
									self.assertEqual(snake_case	,       [{'generated_text': ANY(snake_case						)}]						)
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]['generated_text'].startswith('Something there'						)						)

									_UpperCAmelCase							 =		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						)}],
									    ]	,       )

									_UpperCAmelCase							 =		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						):
											generator(4						)






							@require_torch
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		pipeline('text2text-generation'	,       model='patrickvonplaten/t5-tiny-random'	,       framework='pt'						)
									# do_sample=False necessary for reproducibility
									_UpperCAmelCase							 =		generator('Something there'	,       do_sample=snake_case						)
									self.assertEqual(snake_case	,       [{'generated_text': ''}]						)

									_UpperCAmelCase							 =		3
									_UpperCAmelCase							 =		generator(
									    'Something there'	,       num_return_sequences=snake_case	,       num_beams=snake_case	,       )
									_UpperCAmelCase							 =		[
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
									    {'generated_text': ''},
									]
									self.assertEqual(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		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(torch.Tensor						)},
									        {'generated_token_ids': ANY(torch.Tensor						)},
									    ]	,       )
									_UpperCAmelCase							 =		generator.model.config.eos_token_id
									_UpperCAmelCase							 =		'<pad>'
									_UpperCAmelCase							 =		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(torch.Tensor						)},
									            {'generated_token_ids': ANY(torch.Tensor						)},
									        ],
									        [
									            {'generated_token_ids': ANY(torch.Tensor						)},
									            {'generated_token_ids': ANY(torch.Tensor						)},
									        ],
									    ]	,       )






							@require_tf
							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		pipeline('text2text-generation'	,       model='patrickvonplaten/t5-tiny-random'	,       framework='tf'						)
									# do_sample=False necessary for reproducibility
									_UpperCAmelCase							 =		generator('Something there'	,       do_sample=snake_case						)
									self.assertEqual(snake_case	,       [{'generated_text': ''}]						)


