





"""simple docstring"""






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


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class 	lowerCAmelCase					(      lowercase__							):
       lowerCAmelCase__						 =  """xglm"""
       lowerCAmelCase__						 =  ["""past_key_values"""]

       lowerCAmelCase__						 =  {
           """num_attention_heads""": """attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """num_layers""",
       }






       def __init__(   self    ,  a__=25_60_08    ,  a__=20_48    ,  a__=10_24    ,  a__=40_96    ,  a__=24    ,  a__=16    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=0.0    ,  a__=0.0    ,  a__=0.02    ,  a__=True    ,  a__=True    ,  a__=2    ,  a__=1    ,  a__=0    ,  a__=2    ,  **a__    ,  ):
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     d_model
              _UpperCAmelCase     =     ffn_dim
              _UpperCAmelCase     =     num_layers
              _UpperCAmelCase     =     attention_heads
              _UpperCAmelCase     =     activation_function
              _UpperCAmelCase     =     dropout
              _UpperCAmelCase     =     attention_dropout
              _UpperCAmelCase     =     activation_dropout
              _UpperCAmelCase     =     layerdrop
              _UpperCAmelCase     =     init_std
              _UpperCAmelCase     =     scale_embedding  # scale factor will be sqrt(d_model) if True
              _UpperCAmelCase     =     use_cache

              super().__init__(
                  pad_token_id=__lowercase    ,  bos_token_id=__lowercase    ,  eos_token_id=__lowercase    ,  decoder_start_token_id=__lowercase    ,  **__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 							__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     )






