



"""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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return None



class       SCREAMING_SNAKE_CASE__  :

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    return None



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

   @require_tf
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

   @require_torch
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




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

   @require_torch
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    from transformers import BertModel

    UpperCAmelCase	:					str	=	["""[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	:					int	=	BertTokenizerFast(vocab_file.name						)

    with TemporaryDirectory() as bert_save_dir:
     UpperCAmelCase	:					Dict	=	BertModel(BertConfig(vocab_size=len(__A						)						)						)
     model.save_pretrained(__A						)
     self._test_export(__A      ,     """pt"""      ,     12      ,     __A						)

   @require_tf
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     UpperCAmelCase	:					int	=	self._test_export(__A      ,     """tf"""      ,     12      ,     **__A						)
     UpperCAmelCase	:					int	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     UpperCAmelCase	:					List[str]	=	self._test_export(__A      ,     """pt"""      ,     12      ,     **__A						)
     UpperCAmelCase	:					List[Any]	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    try:
     # Compute path
     with TemporaryDirectory() as tempdir:
      UpperCAmelCase	:					int	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    from transformers import BertModel

    UpperCAmelCase	:					int	=	BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""						)						)
    UpperCAmelCase	:					List[str]	=	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""						)
    self._test_infer_dynamic_axis(__A      ,     __A      ,     """pt"""						)

   @require_tf
   @require_tokenizers
   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    from transformers import TFBertModel

    UpperCAmelCase	:					Optional[Any]	=	TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""						)						)
    UpperCAmelCase	:					Dict	=	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""						)
    self._test_infer_dynamic_axis(__A      ,     __A      ,     """tf"""						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	FeatureExtractionPipeline(__A      ,     __A						)

    UpperCAmelCase	:					Dict	=	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	["""input_ids""", """attention_mask""", """token_type_ids"""]
    UpperCAmelCase	:					int	=	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
    UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	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	,		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 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
