

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					:


       '''simple docstring'''







       def 	lowerCamelCase__    (						self       :	List[str]    ,						__snake_case       :	Union[str, Any]    ,						__snake_case       :	List[str]    ,						__snake_case       :	Dict  ) ->			Dict:







        '''simple docstring'''

        return None


class      lowerCAmelCase					:


       '''simple docstring'''







       def 	lowerCamelCase__    (						self       :	Optional[Any]    ,						__snake_case       :	Optional[int]    ,						__snake_case       :	List[str]    ,						__snake_case       :	List[Any]    ,						__snake_case       :	Tuple  ) ->			List[str]:







        '''simple docstring'''

        return None


class      lowerCAmelCase					(     unittest.TestCase					):


       '''simple docstring'''







       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 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			List[str]:







        '''simple docstring'''

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

       @require_torch
       @slow
       def 	lowerCamelCase__    (						self       :	Union[str, Any]  ) ->			List[str]:







        '''simple docstring'''

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

       @require_torch
       @slow
       def 	lowerCamelCase__    (						self       :	Any  ) ->			Dict:







        '''simple docstring'''

        from transformers import BertModel

        lowerCamelCase       =     ['[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()
         lowerCamelCase       =     BertTokenizerFast(vocab_file.name  )

        with TemporaryDirectory() as bert_save_dir:
         lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	int  ) ->			Optional[int]:







        '''simple docstring'''

        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowerCamelCase       =     self._test_export(__snake_case    ,						'tf'    ,						12    ,						**__snake_case  )
         lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	Tuple  ) ->			Tuple:







        '''simple docstring'''

        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         lowerCamelCase       =     self._test_export(__snake_case    ,						'pt'    ,						12    ,						**__snake_case  )
         lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	Optional[int]    ,						__snake_case       :	str    ,						__snake_case       :	Any    ,						__snake_case       :	int    ,						__snake_case       :	Union[str, Any]=None    ,						**__snake_case       :	Union[str, Any]  ) ->			Optional[int]:







        '''simple docstring'''

        try:
         # Compute path
         with TemporaryDirectory() as tempdir:
          lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	List[str]  ) ->			Union[str, Any]:







        '''simple docstring'''

        from transformers import BertModel

        lowerCamelCase       =     BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'  )  )
        lowerCamelCase       =     BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'  )
        self._test_infer_dynamic_axis(__snake_case    ,						__snake_case    ,						'pt'  )

       @require_tf
       @require_tokenizers
       @slow
       def 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			str:







        '''simple docstring'''

        from transformers import TFBertModel

        lowerCamelCase       =     TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'  )  )
        lowerCamelCase       =     BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'  )
        self._test_infer_dynamic_axis(__snake_case    ,						__snake_case    ,						'tf'  )

       def 	lowerCamelCase__    (						self       :	List[Any]    ,						__snake_case       :	List[str]    ,						__snake_case       :	List[Any]    ,						__snake_case       :	str  ) ->			Dict:







        '''simple docstring'''

        lowerCamelCase       =     FeatureExtractionPipeline(__snake_case    ,						__snake_case  )

        lowerCamelCase       =     ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
        lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	int  ) ->			Any:







        '''simple docstring'''

        lowerCamelCase       =     ['input_ids', 'attention_mask', 'token_type_ids']
        lowerCamelCase       =     {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
        lowerCamelCase       =     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)
        lowerCamelCase       =     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 	lowerCamelCase__    (						self       :	Any  ) ->			str:







        '''simple docstring'''

        lowerCamelCase       =     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"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}