



"""simple docstring"""






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


__snake_case							:   List[Any]      =    logging.get_logger(__name__)

__snake_case							:   Optional[Any]      =    {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'openai-gpt'
	SCREAMING_SNAKE_CASE          =	{
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}

	def __init__(     self:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=4_0478       ,			_SCREAMING_SNAKE_CASE:		List[Any]=512       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=768       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=12       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=12       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.1       ,			_SCREAMING_SNAKE_CASE:		int=0.1       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.1       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1e-5       ,			_SCREAMING_SNAKE_CASE:		List[str]=0.02       ,			_SCREAMING_SNAKE_CASE:		List[Any]="cls_index"       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=True       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=True       ,			_SCREAMING_SNAKE_CASE:		int=0.1       ,			**_SCREAMING_SNAKE_CASE:		Tuple       ,			)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       vocab_size
				__lowerCAmelCase      :  List[Any]												=       n_positions
				__lowerCAmelCase      :  Any												=       n_embd
				__lowerCAmelCase      :  Dict												=       n_layer
				__lowerCAmelCase      :  List[str]												=       n_head
				__lowerCAmelCase      :  Any												=       afn
				__lowerCAmelCase      :  int												=       resid_pdrop
				__lowerCAmelCase      :  Any												=       embd_pdrop
				__lowerCAmelCase      :  int												=       attn_pdrop
				__lowerCAmelCase      :  List[str]												=       layer_norm_epsilon
				__lowerCAmelCase      :  int												=       initializer_range
				__lowerCAmelCase      :  List[Any]												=       summary_type
				__lowerCAmelCase      :  Dict												=       summary_use_proj
				__lowerCAmelCase      :  Tuple												=       summary_activation
				__lowerCAmelCase      :  List[str]												=       summary_first_dropout
				__lowerCAmelCase      :  Dict												=       summary_proj_to_labels
				super().__init__(**_SCREAMING_SNAKE_CASE)




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)