





"""simple docstring"""






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


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """markuplm"""






       def __init__(   self    ,  a__=3_05_22    ,  a__=7_68    ,  a__=12    ,  a__=12    ,  a__=30_72    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=2    ,  a__=0.02    ,  a__=1E-12    ,  a__=0    ,  a__=0    ,  a__=2    ,  a__=2_56    ,  a__=10_24    ,  a__=2_16    ,  a__=10_01    ,  a__=32    ,  a__=50    ,  a__="absolute"    ,  a__=True    ,  a__=None    ,  **a__    ,  ):
              super().__init__(
                  pad_token_id=a__    ,  bos_token_id=a__    ,  eos_token_id=a__    ,  **a__    ,  )
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     layer_norm_eps
              _UpperCAmelCase     =     position_embedding_type
              _UpperCAmelCase     =     use_cache
              _UpperCAmelCase     =     classifier_dropout
              # additional properties
              _UpperCAmelCase     =     max_depth
              _UpperCAmelCase     =     max_xpath_tag_unit_embeddings
              _UpperCAmelCase     =     max_xpath_subs_unit_embeddings
              _UpperCAmelCase     =     tag_pad_id
              _UpperCAmelCase     =     subs_pad_id
              _UpperCAmelCase     =     xpath_unit_hidden_size













"""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 							__A (   self     ):
              _UpperCAmelCase     =     FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'     )
              _UpperCAmelCase     =     AutoTokenizer.from_pretrained('google/mt5-small'     )

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

              _UpperCAmelCase     =     shift_tokens_right(a__    ,  model.config.pad_token_id    ,  model.config.decoder_start_token_id     )

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

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

              _UpperCAmelCase     =     -84.9_127
              self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )






