"""simple docstring"""





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


lowerCAmelCase_     			=						logging.get_logger(__name__)

lowerCAmelCase_     			=						{
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class 			_snake_case		(     __snake_case			):







				"""simple docstring"""






				a      =    'decision_transformer'
				a      =    ['past_key_values']
				a      =    {
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}





				def __init__(   self				:				Optional[Any]   ,     _A				:				Any=1_7   ,     _A				:				str=4   ,     _A				:				Tuple=1_2_8   ,     _A				:				int=4_0_9_6   ,     _A				:				str=True   ,     _A				:				List[str]=1   ,     _A				:				int=1_0_2_4   ,     _A				:				List[str]=3   ,     _A				:				int=1   ,     _A				:				Optional[Any]=None   ,     _A				:				Union[str, Any]="relu"   ,     _A				:				Union[str, Any]=0.1   ,     _A				:				Optional[Any]=0.1   ,     _A				:				Dict=0.1   ,     _A				:				Any=1e-5   ,     _A				:				List[Any]=0.02   ,     _A				:				Tuple=True   ,     _A				:				Optional[Any]=True   ,     _A				:				Dict=5_0_2_5_6   ,     _A				:				Optional[int]=5_0_2_5_6   ,     _A				:				Union[str, Any]=False   ,     _A				:				Any=False   ,     **_A				:				Optional[Any]   ,     ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Any      	=     state_dim
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     act_dim
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     hidden_size
											_SCREAMING_SNAKE_CASE		:    Any      	=     max_ep_len
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     action_tanh
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     vocab_size
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     n_positions
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     n_layer
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     n_head
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     n_inner
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     activation_function
											_SCREAMING_SNAKE_CASE		:    Any      	=     resid_pdrop
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     embd_pdrop
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     attn_pdrop
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     layer_norm_epsilon
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     initializer_range
											_SCREAMING_SNAKE_CASE		:    Any      	=     scale_attn_weights
											_SCREAMING_SNAKE_CASE		:    Dict      	=     use_cache
											_SCREAMING_SNAKE_CASE		:    Dict      	=     scale_attn_by_inverse_layer_idx
											_SCREAMING_SNAKE_CASE		:    Dict      	=     reorder_and_upcast_attn

											_SCREAMING_SNAKE_CASE		:    Any      	=     bos_token_id
											_SCREAMING_SNAKE_CASE		:    Dict      	=     eos_token_id

											super().__init__(bos_token_id=_A   ,     eos_token_id=_A   ,     **_A)




"""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  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



