






"""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	:





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		return None






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		return None






class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Any 			=					[
	    # (model_name, model_kwargs)
	    ("""bert-base-cased""", {}),
	    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def  lowerCamelCase(self				):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(lowerCAmelCase_  ,  """tf"""  ,  12  ,  **lowerCAmelCase_				)





	@require_torch
	@slow
	def  lowerCamelCase(self				):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(lowerCAmelCase_  ,  """pt"""  ,  12  ,  **lowerCAmelCase_				)





	@require_torch
	@slow
	def  lowerCamelCase(self				):
		from transformers import BertModel

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

		with TemporaryDirectory() as bert_save_dir:
			A_		:    str						     = BertModel(BertConfig(vocab_size=len(lowerCAmelCase_				)				)				)
			model.save_pretrained(lowerCAmelCase_				)
			self._test_export(lowerCAmelCase_  ,  """pt"""  ,  12  ,  lowerCAmelCase_				)





	@require_tf
	@slow
	def  lowerCamelCase(self				):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			A_		:    List[Any]						     = self._test_export(lowerCAmelCase_  ,  """tf"""  ,  12  ,  **lowerCAmelCase_				)
			A_		:    List[Any]						     = quantize(Path(lowerCAmelCase_				)				)

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





	@require_torch
	@slow
	def  lowerCamelCase(self				):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			A_		:    List[Any]						     = self._test_export(lowerCAmelCase_  ,  """pt"""  ,  12  ,  **lowerCAmelCase_				)
			A_		:    str						     = quantize(lowerCAmelCase_				)

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				A_		:    Union[str, Any]						     = Path(lowerCAmelCase_				).joinpath("""model.onnx"""				)

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

			# Export
			convert(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				)

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





	@require_torch
	@require_tokenizers
	@slow
	def  lowerCamelCase(self				):
		from transformers import BertModel

		A_		:    Optional[int]						     = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
		A_		:    Union[str, Any]						     = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
		self._test_infer_dynamic_axis(lowerCAmelCase_  ,  lowerCAmelCase_  ,  """pt"""				)





	@require_tf
	@require_tokenizers
	@slow
	def  lowerCamelCase(self				):
		from transformers import TFBertModel

		A_		:    Optional[Any]						     = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
		A_		:    List[str]						     = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
		self._test_infer_dynamic_axis(lowerCAmelCase_  ,  lowerCAmelCase_  ,  """tf"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = FeatureExtractionPipeline(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[Any]						     = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
		A_     ,  A_     ,  A_     ,  A_		:    Any						     = infer_shapes(lowerCAmelCase_  ,  lowerCAmelCase_				)

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

		# 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  lowerCamelCase(self				):
		A_		:    Dict						     = ["""input_ids""", """attention_mask""", """token_type_ids"""]
		A_		:    int						     = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
		A_     ,  A_		:    int						     = ensure_valid_input(FuncContiguousArgs()  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

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

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

		# Parameter should be reordered according to their respective place in the function:
		# (input_ids, token_type_ids, attention_mask)
		self.assertEqual(lowerCAmelCase_  ,  (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_		:    Union[str, Any]						     = ensure_valid_input(FuncNonContiguousArgs()  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

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

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





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = 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 typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

