





"""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  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=99   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__=5   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=37   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=4   ,      )			->				Any:
								a   :	Dict			=       parent
								a   :	Optional[int]			=       batch_size
								a   :	Tuple			=       seq_length
								a   :	Optional[Any]			=       is_training
								a   :	List[str]			=       use_attention_mask
								a   :	Optional[Any]			=       use_token_type_ids
								a   :	Tuple			=       use_labels
								a   :	Any			=       vocab_size
								a   :	List[Any]			=       hidden_size
								a   :	str			=       num_hidden_layers
								a   :	Tuple			=       num_attention_heads
								a   :	Optional[Any]			=       intermediate_size
								a   :	Union[str, Any]			=       hidden_act
								a   :	List[str]			=       hidden_dropout_prob
								a   :	Optional[Any]			=       attention_probs_dropout_prob
								a   :	Optional[Any]			=       max_position_embeddings
								a   :	Any			=       type_vocab_size
								a   :	List[str]			=       type_sequence_label_size
								a   :	List[Any]			=       initializer_range
								a   :	Optional[int]			=       num_choices






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Any			=       ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size   )

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

								a   :	str			=       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_=lowerCAmelCase__   ,      )

								return config, input_ids, attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Union[str, Any]			=       self.prepare_config_and_inputs()
								a, a, a   :	Optional[Any]			=       config_and_inputs
								a   :	str			=       {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =(
							    (
							        FlaxDistilBertModel,
							        FlaxDistilBertForMaskedLM,
							        FlaxDistilBertForMultipleChoice,
							        FlaxDistilBertForQuestionAnswering,
							        FlaxDistilBertForSequenceClassification,
							        FlaxDistilBertForTokenClassification,
							        FlaxDistilBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					__a     (     self   )			->				int:
								a   :	str			=       FlaxDistilBertModelTester(self   )






							@slow
							def 					__a     (     self   )			->				List[str]:
								for model_class_name in self.all_model_classes:
									a   :	Any			=       model_class_name.from_pretrained("distilbert-base-uncased"   )
									a   :	str			=       model(np.ones((1, 1)   )   )
									self.assertIsNotNone(lowerCAmelCase__   )


@require_flax
class  __UpperCamelCase       (		unittest.TestCase		):






							@slow
							def 					__a     (     self   )			->				Optional[int]:
								a   :	int			=       FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"   )
								a   :	Dict			=       np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
								a   :	str			=       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
								a   :	Optional[int]			=       model(lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   )[0]
								a   :	List[Any]			=       (1, 11, 768)
								self.assertEqual(output.shape   ,      lowerCAmelCase__   )
								a   :	List[str]			=       np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]   )

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









"""simple docstring"""




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


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''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  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

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


