





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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		Tuple					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		List[str]=1_3					,  lowerCAmelCase_ :		Any=7					,  lowerCAmelCase_ :		str=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Optional[Any]=True					,  lowerCAmelCase_ :		int=True					,  lowerCAmelCase_ :		Optional[Any]=9_9					,  lowerCAmelCase_ :		Optional[int]=3_2					,  lowerCAmelCase_ :		Optional[Any]=5					,  lowerCAmelCase_ :		List[Any]=4					,  lowerCAmelCase_ :		Optional[int]=3_7					,  lowerCAmelCase_ :		List[str]="gelu"					,  lowerCAmelCase_ :		Tuple=0.1					,  lowerCAmelCase_ :		Dict=0.1					,  lowerCAmelCase_ :		List[Any]=5_1_2					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Optional[Any]=2					,  lowerCAmelCase_ :		Optional[Any]=0.02					,  lowerCAmelCase_ :		Dict=4					,  )							->						Any:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	seq_length
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_attention_mask
     __lowerCAmelCase								=	use_token_type_ids
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	type_vocab_size
     __lowerCAmelCase								=	type_sequence_label_size
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	num_choices






  def 				lowercase		(				self :		int     )							->						Any:
     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size     )

     __lowerCAmelCase								=	None
     if self.use_attention_mask:
        __lowerCAmelCase								=	random_attention_mask([self.batch_size, self.seq_length]     )

     __lowerCAmelCase								=	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_=UpperCamelCase_					,  )

     return config, input_ids, attention_mask






  def 				lowercase		(				self :		int     )							->						List[str]:
     __lowerCAmelCase								=	self.prepare_config_and_inputs()
     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	config_and_inputs
     __lowerCAmelCase								=	{'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict





@require_flax
class 				_UpperCAmelCase    (    __lowerCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (
      (
          FlaxDistilBertModel,
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	FlaxDistilBertModelTester(self     )






  @slow
  def 				lowercase		(				self :		Any     )							->						List[str]:
     for model_class_name in self.all_model_classes:
        __lowerCAmelCase								=	model_class_name.from_pretrained('distilbert-base-uncased'     )
        __lowerCAmelCase								=	model(np.ones((1, 1)     )     )
        self.assertIsNotNone(UpperCamelCase_     )





@require_flax
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @slow
  def 				lowercase		(				self :		List[Any]     )							->						List[Any]:
     __lowerCAmelCase								=	FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'     )
     __lowerCAmelCase								=	np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
     __lowerCAmelCase								=	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
     __lowerCAmelCase								=	model(UpperCamelCase_					,  attention_mask=UpperCamelCase_     )[0]
     __lowerCAmelCase								=	(1, 1_1, 7_6_8)
     self.assertEqual(output.shape					,  UpperCamelCase_     )
     __lowerCAmelCase								=	np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]     )

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







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}





class     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

             __lowerCamelCase   =						self.model_save_dir.joinpath(self.latest_model_name   )
             __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
             try:
                    shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
             except shutil.SameFileError:
                    pass

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[Any]    ,       ):
             if os.path.isfile(UpperCamelCase_   ):
                    logger.error(F'Provided path ({save_directory}) should be a directory, not a file'   )
                    return

             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )

             # saving model weights/files
             self._save_pretrained(UpperCamelCase_    ,       **UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: str    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[Union[bool, str, None]] = None    ,       UpperCamelCase_: Optional[Union[str, None]] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional["ort.SessionOptions"] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

             return cls._from_pretrained(
                 model_id=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

