'''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  __magic_name__  :





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    return None




class  __magic_name__  :





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Tuple		,  _lowercase					:  str		,  _lowercase					:  str		,  _lowercase					:  str							):



    """simple docstring"""



    return None




class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[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					:  Optional[Any]							):



    """simple docstring"""



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



   @require_torch
   @slow
   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



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



   @require_torch
   @slow
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    from transformers import BertModel

    _UpperCamelCase:     int							= ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
    with NamedTemporaryFile(mode='''w+t'''							) as vocab_file:
     vocab_file.write('''\n'''.join(__a							)							)
     vocab_file.flush()
     _UpperCamelCase:     Dict							= BertTokenizerFast(vocab_file.name							)

    with TemporaryDirectory() as bert_save_dir:
     _UpperCamelCase:     List[str]							= BertModel(BertConfig(vocab_size=len(__a							)							)							)
     model.save_pretrained(__a							)
     self._test_export(__a		,  '''pt'''		,  12		,  __a							)



   @require_tf
   @slow
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     _UpperCamelCase:     Optional[int]							= self._test_export(__a		,  '''tf'''		,  12		,  **__a							)
     _UpperCamelCase:     Tuple							= quantize(Path(__a							)							)

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



   @require_torch
   @slow
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     _UpperCamelCase:     Any							= self._test_export(__a		,  '''pt'''		,  12		,  **__a							)
     _UpperCamelCase:     Dict							= quantize(__a							)

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



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  List[Any]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[Any]=None		,  **_lowercase					:  Optional[int]							):



    """simple docstring"""



    try:
     # Compute path
     with TemporaryDirectory() as tempdir:
      _UpperCamelCase:     Optional[Any]							= Path(__a							).joinpath('''model.onnx'''							)

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

     # Export
     convert(__a		,  __a		,  __a		,  __a		,  __a		,  **__a							)

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



   @require_torch
   @require_tokenizers
   @slow
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    from transformers import BertModel

    _UpperCamelCase:     int							= BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''							)							)
    _UpperCamelCase:     List[Any]							= BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''							)
    self._test_infer_dynamic_axis(__a		,  __a		,  '''pt'''							)



   @require_tf
   @require_tokenizers
   @slow
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    from transformers import TFBertModel

    _UpperCamelCase:     Union[str, Any]							= TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''							)							)
    _UpperCamelCase:     str							= BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''							)
    self._test_infer_dynamic_axis(__a		,  __a		,  '''tf'''							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  str		,  _lowercase					:  Optional[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= FeatureExtractionPipeline(__a		,  __a							)

    _UpperCamelCase:     List[str]							= ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
    _UpperCamelCase:     Tuple							= infer_shapes(__a		,  __a							)

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

    # 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					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= ["""input_ids""", """attention_mask""", """token_type_ids"""]
    _UpperCamelCase:     str							= {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
    _UpperCamelCase:     List[Any]							= ensure_valid_input(FuncContiguousArgs()		,  __a		,  __a							)

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

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

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

    # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
    _UpperCamelCase:     List[Any]							= ensure_valid_input(FuncNonContiguousArgs()		,  __a		,  __a							)

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



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''							)		,  '''-test'''							)
    self.assertEqual('''/home/something/my_fake_model-test.onnx'''		,  generated.as_posix()							)
from __future__ import annotations




def       lowerCAmelCase_     (		lowercase:							int = 4		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= abs(lowercase		) or 4
 return [[1 + x + y * row_size for x in range(lowercase		)] for y in range(lowercase		)]




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(transpose(lowercase		)		)
 # OR.. transpose(reverse_column(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(reverse_column(lowercase		)		)
 # OR.. reverse_column(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_column(transpose(lowercase		)		)
 # OR.. transpose(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     int							= [list(lowercase		) for x in zip(*lowercase		)]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= matrix[::-1]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= [x[::-1] for x in matrix]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				None:







 '''simple docstring'''
 for i in matrix:
  print(*lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 90 counterclockwise:\n''')
  print_matrix(rotate_aa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 180:\n''')
  print_matrix(rotate_aaa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 270 counterclockwise:\n''')
  print_matrix(rotate_aaa(matrix))