





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    lowerCAmelCase							:
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Any	)			->	str:
     return None


class    lowerCAmelCase							:
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Dict	)			->	Union[str, Any]:
     return None


class    lowerCAmelCase							(					unittest.TestCase			):
 UpperCAmelCase__													=		[
     # (model_name, model_kwargs)
     ("""bert-base-cased""", {}),
     ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
 ]
 @require_tf
 @slow
 def      A_	(      self  :      int	)			->	Optional[int]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_			,     'tf'			,     12			,     **lowercase_	)
 @require_torch
 @slow
 def      A_	(      self  :      Optional[int]	)			->	Dict:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(lowercase_			,     'pt'			,     12			,     **lowercase_	)
 @require_torch
 @slow
 def      A_	(      self  :      str	)			->	List[str]:
     from transformers import BertModel

     lowerCamelCase__    :      str					 =		['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
     with NamedTemporaryFile(mode='w+t'	) as vocab_file:
         vocab_file.write('\n'.join(lowercase_	)	)
         vocab_file.flush()
         lowerCamelCase__    :      List[Any]					 =		BertTokenizerFast(vocab_file.name	)

     with TemporaryDirectory() as bert_save_dir:
         lowerCamelCase__    :      List[str]					 =		BertModel(BertConfig(vocab_size=len(lowercase_	)	)	)
         model.save_pretrained(lowercase_	)
         self._test_export(lowercase_			,     'pt'			,     12			,     lowercase_	)
 @require_tf
 @slow
 def      A_	(      self  :      Dict	)			->	Union[str, Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowerCamelCase__    :      Optional[int]					 =		self._test_export(lowercase_			,     'tf'			,     12			,     **lowercase_	)
         lowerCamelCase__    :      Union[str, Any]					 =		quantize(Path(lowercase_	)	)

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowercase_	).stat().st_size:
             self.fail('Quantized model is bigger than initial ONNX model'	)
 @require_torch
 @slow
 def      A_	(      self  :      Union[str, Any]	)			->	Union[str, Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowerCamelCase__    :      Any					 =		self._test_export(lowercase_			,     'pt'			,     12			,     **lowercase_	)
         lowerCamelCase__    :      str					 =		quantize(lowercase_	)

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(lowercase_	).stat().st_size:
             self.fail('Quantized model is bigger than initial ONNX model'	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      int=None			,     **UpperCAmelCase  :      str	)			->	Optional[int]:
     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             lowerCamelCase__    :      Tuple					 =		Path(lowercase_	).joinpath('model.onnx'	)

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

         # Export
         convert(lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     **lowercase_	)

         return path
     except Exception as e:
         self.fail(lowercase_	)
 @require_torch
 @require_tokenizers
 @slow
 def      A_	(      self  :      Optional[Any]	)			->	Any:
     from transformers import BertModel

     lowerCamelCase__    :      int					 =		BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'	)	)
     lowerCamelCase__    :      Union[str, Any]					 =		BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'	)
     self._test_infer_dynamic_axis(lowercase_			,     lowercase_			,     'pt'	)
 @require_tf
 @require_tokenizers
 @slow
 def      A_	(      self  :      List[Any]	)			->	Optional[Any]:
     from transformers import TFBertModel

     lowerCamelCase__    :      List[Any]					 =		TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'	)	)
     lowerCamelCase__    :      Union[str, Any]					 =		BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'	)
     self._test_infer_dynamic_axis(lowercase_			,     lowercase_			,     'tf'	)
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      str	)			->	Tuple:
     lowerCamelCase__    :      List[str]					 =		FeatureExtractionPipeline(lowercase_			,     lowercase_	)

     lowerCamelCase__    :      str					 =		['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		infer_shapes(lowercase_			,     lowercase_	)

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

     # 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      A_	(      self  :      Union[str, Any]	)			->	Any:
     lowerCamelCase__    :      Any					 =		['input_ids', 'attention_mask', 'token_type_ids']
     lowerCamelCase__    :      Tuple					 =		{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
     lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		ensure_valid_input(FuncContiguousArgs()			,     lowercase_			,     lowercase_	)

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

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

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

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		ensure_valid_input(FuncNonContiguousArgs()			,     lowercase_			,     lowercase_	)

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

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







 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     lowerCamelCase__    :      List[str]					 =		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 __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



