





'''simple docstring'''

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


UpperCAmelCase					:		Union[str, Any]		=							logging.get_logger(__name__)

UpperCAmelCase					:		int		=							{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				"""lxmert"""
  _lowercase  :	Optional[int]       					=				{}







  def __init__(		self     ,     lowercase__=30_522     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=9_500     ,     lowercase__=1_600     ,     lowercase__=400     ,     lowercase__=3_072     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=512     ,     lowercase__=2     ,     lowercase__=0.02     ,     lowercase__=1E-1_2     ,     lowercase__=9     ,     lowercase__=5     ,     lowercase__=5     ,     lowercase__=2_048     ,     lowercase__=4     ,     lowercase__=6.67     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     vocab_size
        _snake_case		:	Dict					     =     hidden_size
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Union[str, Any]					     =     hidden_act
        _snake_case		:	int					     =     intermediate_size
        _snake_case		:	Union[str, Any]					     =     hidden_dropout_prob
        _snake_case		:	Dict					     =     attention_probs_dropout_prob
        _snake_case		:	Optional[Any]					     =     max_position_embeddings
        _snake_case		:	int					     =     type_vocab_size
        _snake_case		:	int					     =     initializer_range
        _snake_case		:	Any					     =     layer_norm_eps
        _snake_case		:	Optional[Any]					     =     num_qa_labels
        _snake_case		:	str					     =     num_object_labels
        _snake_case		:	List[Any]					     =     num_attr_labels
        _snake_case		:	Optional[Any]					     =     l_layers
        _snake_case		:	Optional[int]					     =     x_layers
        _snake_case		:	Dict					     =     r_layers
        _snake_case		:	str					     =     visual_feat_dim
        _snake_case		:	Union[str, Any]					     =     visual_pos_dim
        _snake_case		:	Any					     =     visual_loss_normalizer
        _snake_case		:	int					     =     task_matched
        _snake_case		:	Optional[int]					     =     task_mask_lm
        _snake_case		:	Tuple					     =     task_obj_predict
        _snake_case		:	Tuple					     =     task_qa
        _snake_case		:	List[str]					     =     visual_obj_loss
        _snake_case		:	Optional[int]					     =     visual_attr_loss
        _snake_case		:	str					     =     visual_feat_loss
        _snake_case		:	Union[str, Any]					     =     {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
        super().__init__(**lowercase__		)









'''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		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


