
"""simple docstring"""




import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.distilbert.modeling_flax_distilbert import (
       FlaxDistilBertForMaskedLM,
       FlaxDistilBertForMultipleChoice,
       FlaxDistilBertForQuestionAnswering,
       FlaxDistilBertForSequenceClassification,
       FlaxDistilBertForTokenClassification,
       FlaxDistilBertModel,
   )

class 						a__		( unittest.TestCase		):
   def __init__(     self      ,       _a      ,       _a=13      ,       _a=7      ,       _a=True      ,       _a=True      ,       _a=True      ,       _a=True      ,       _a=99      ,       _a=32      ,       _a=5      ,       _a=4      ,       _a=37      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=512      ,       _a=16      ,       _a=2      ,       _a=0.0_2      ,       _a=4      ,       ):
          lowercase       :							Union[str, Any]									=				parent
          lowercase       :							Any									=				batch_size
          lowercase       :							str									=				seq_length
          lowercase       :							str									=				is_training
          lowercase       :							Optional[Any]									=				use_attention_mask
          lowercase       :							List[str]									=				use_token_type_ids
          lowercase       :							Any									=				use_labels
          lowercase       :							str									=				vocab_size
          lowercase       :							int									=				hidden_size
          lowercase       :							Tuple									=				num_hidden_layers
          lowercase       :							Union[str, Any]									=				num_attention_heads
          lowercase       :							Optional[Any]									=				intermediate_size
          lowercase       :							Union[str, Any]									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							List[Any]									=				max_position_embeddings
          lowercase       :							List[str]									=				type_vocab_size
          lowercase       :							int									=				type_sequence_label_size
          lowercase       :							List[Any]									=				initializer_range
          lowercase       :							Optional[Any]									=				num_choices
   def      __magic_name__     (     self							):
          lowercase       :							str									=				ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

          lowercase       :							Optional[int]									=				None
          if self.use_attention_mask:
                 lowercase       :							Optional[int]									=				random_attention_mask([self.batch_size, self.seq_length]							)

          lowercase       :							Dict									=				DistilBertConfig(
              vocab_size=self.vocab_size      ,       dim=self.hidden_size      ,       n_layers=self.num_hidden_layers      ,       n_heads=self.num_attention_heads      ,       hidden_dim=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       dropout=self.hidden_dropout_prob      ,       attention_dropout=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       initializer_range=self.initializer_range      ,       tie_weights_=_a      ,       )

          return config, input_ids, attention_mask

   def      __magic_name__     (     self							):
          lowercase       :							int									=				self.prepare_config_and_inputs()
          lowercase						,       lowercase						,       lowercase       :							str									=				config_and_inputs
          lowercase       :							Optional[Any]									=				{"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict


@require_flax
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							(
       (
           FlaxDistilBertModel,
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				FlaxDistilBertModelTester(self							)

   @slow
   def      __magic_name__     (     self							):
          for model_class_name in self.all_model_classes:
                 lowercase       :							List[Any]									=				model_class_name.from_pretrained("distilbert-base-uncased"							)
                 lowercase       :							int									=				model(np.ones((1, 1)							)							)
                 self.assertIsNotNone(_a							)


@require_flax
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
          lowercase       :							List[str]									=				np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]							)
          lowercase       :							Optional[int]									=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
          lowercase       :							str									=				model(_a      ,       attention_mask=_a							)[0]
          lowercase       :							Optional[Any]									=				(1, 11, 768)
          self.assertEqual(output.shape      ,       _a							)
          lowercase       :							Optional[int]									=				np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]							)

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,       _a      ,       atol=1E-4							)							)




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


