






"""simple docstring"""



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class 							__SCREAMING_SNAKE_CASE						:


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Optional[int]			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													return None






class 							__SCREAMING_SNAKE_CASE						:


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						Dict			,    snake_case      :						int							):
													'''simple docstring'''





													return None






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
							snake_case_     =							[
							    # (model_name, model_kwargs)
							    ('bert-base-cased', {}),
							    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
							]


							@require_tf
							@slow
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																			self._test_export(snake_case			,    """tf"""			,    12			,    **snake_case							)


							@require_torch
							@slow
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																			self._test_export(snake_case			,    """pt"""			,    12			,    **snake_case							)


							@require_torch
							@slow
							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													from transformers import BertModel

													A__						:  Union[str, Any]		  =  ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
													with NamedTemporaryFile(mode="""w+t"""							) as vocab_file:
																			vocab_file.write("""\n""".join(snake_case							)							)
																			vocab_file.flush()
																			A__						:  Optional[int]		  =  BertTokenizerFast(vocab_file.name							)

													with TemporaryDirectory() as bert_save_dir:
																			A__						:  List[Any]		  =  BertModel(BertConfig(vocab_size=len(snake_case							)							)							)
																			model.save_pretrained(snake_case							)
																			self._test_export(snake_case			,    """pt"""			,    12			,    snake_case							)


							@require_tf
							@slow
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																			A__						:  Dict		  =  self._test_export(snake_case			,    """tf"""			,    12			,    **snake_case							)
																			A__						:  List[Any]		  =  quantize(Path(snake_case							)							)

																			# Ensure the actual quantized model is not bigger than the original one
																			if quantized_path.stat().st_size >= Path(snake_case							).stat().st_size:
																									self.fail("""Quantized model is bigger than initial ONNX model"""							)


							@require_torch
							@slow
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																			A__						:  Optional[Any]		  =  self._test_export(snake_case			,    """pt"""			,    12			,    **snake_case							)
																			A__						:  Optional[Any]		  =  quantize(snake_case							)

																			# Ensure the actual quantized model is not bigger than the original one
																			if quantized_path.stat().st_size >= Path(snake_case							).stat().st_size:
																									self.fail("""Quantized model is bigger than initial ONNX model"""							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						str			,    snake_case      :						str			,    snake_case      :						List[str]			,    snake_case      :						Optional[Any]=None			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													try:
																			# Compute path
																			with TemporaryDirectory() as tempdir:
																									A__						:  int		  =  Path(snake_case							).joinpath("""model.onnx"""							)

																			# Remove folder if exists
																			if path.parent.exists():
																									path.parent.rmdir()

																			# Export
																			convert(snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case							)

																			return path
													except Exception as e:
																			self.fail(snake_case							)


							@require_torch
							@require_tokenizers
							@slow
							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													from transformers import BertModel

													A__						:  str		  =  BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""							)							)
													A__						:  Any		  =  BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""							)
													self._test_infer_dynamic_axis(snake_case			,    snake_case			,    """pt"""							)


							@require_tf
							@require_tokenizers
							@slow
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													from transformers import TFBertModel

													A__						:  str		  =  TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""							)							)
													A__						:  str		  =  BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""							)
													self._test_infer_dynamic_axis(snake_case			,    snake_case			,    """tf"""							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Tuple			,    snake_case      :						str			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													A__						:  int		  =  FeatureExtractionPipeline(snake_case			,    snake_case							)

													A__						:  List[str]		  =  ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
													A__  ,  A__  ,  A__  ,  A__						:  List[Any]		  =  infer_shapes(snake_case			,    snake_case							)

													# Assert all variables are present
													self.assertEqual(len(snake_case							)			,    len(snake_case							)							)
													self.assertTrue(all(var_name in shapes for var_name in variable_names							)							)
													self.assertSequenceEqual(variable_names[:3]			,    snake_case							)
													self.assertSequenceEqual(variable_names[3:]			,    snake_case							)

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																			self.assertDictEqual(shapes[var_name]			,    {0: """batch""", 1: """sequence"""}							)

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes["""output_0"""]			,    {0: """batch""", 1: """sequence"""}							)
													self.assertDictEqual(shapes["""output_1"""]			,    {0: """batch"""}							)


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  str		  =  ["""input_ids""", """attention_mask""", """token_type_ids"""]
													A__						:  Union[str, Any]		  =  {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
													A__  ,  A__						:  Tuple		  =  ensure_valid_input(FuncContiguousArgs()			,    snake_case			,    snake_case							)

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(snake_case							)			,    3							)

													# Should have exactly the same input names
													self.assertEqual(set(snake_case							)			,    set(snake_case							)							)

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(snake_case			,    (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])							)

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__  ,  A__						:  int		  =  ensure_valid_input(FuncNonContiguousArgs()			,    snake_case			,    snake_case							)

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(snake_case							)			,    1							)
													self.assertEqual(len(snake_case							)			,    1							)

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]			,    tokens["""input_ids"""]							)
													self.assertEqual(ordered_input_names[0]			,    """input_ids"""							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Dict		  =  generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""							)			,    """-test"""							)
													self.assertEqual("""/home/something/my_fake_model-test.onnx"""			,    generated.as_posix()							)














"""simple docstring"""



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


A_    =							logging.get_logger(__name__)

A_    =							{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'roc_bert'


							def __init__(			self      :						Tuple			,    snake_case      :						Union[str, Any]=3_0522			,    snake_case      :						Optional[Any]=768			,    snake_case      :						Any=12			,    snake_case      :						Tuple=12			,    snake_case      :						Any=3072			,    snake_case      :						Tuple="gelu"			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						List[str]=512			,    snake_case      :						int=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						str=1e-12			,    snake_case      :						int=True			,    snake_case      :						Optional[Any]=0			,    snake_case      :						str="absolute"			,    snake_case      :						int=None			,    snake_case      :						int=True			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=768			,    snake_case      :						Dict=910			,    snake_case      :						Tuple=512			,    snake_case      :						Tuple=2_4858			,    snake_case      :						Optional[Any]=True			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  hidden_size
													A__						:  Optional[int]		  =  num_hidden_layers
													A__						:  Optional[Any]		  =  num_attention_heads
													A__						:  int		  =  intermediate_size
													A__						:  Optional[Any]		  =  hidden_act
													A__						:  Any		  =  hidden_dropout_prob
													A__						:  Tuple		  =  attention_probs_dropout_prob
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  str		  =  type_vocab_size
													A__						:  Any		  =  layer_norm_eps
													A__						:  List[str]		  =  use_cache
													A__						:  List[str]		  =  enable_pronunciation
													A__						:  int		  =  enable_shape
													A__						:  Tuple		  =  pronunciation_embed_dim
													A__						:  str		  =  pronunciation_vocab_size
													A__						:  Optional[Any]		  =  shape_embed_dim
													A__						:  str		  =  shape_vocab_size
													A__						:  List[str]		  =  concat_input
													A__						:  str		  =  position_embedding_type
													A__						:  str		  =  classifier_dropout
													super().__init__(pad_token_id=snake_case			,    **snake_case							)






