



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     _A       :



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return None


class     _A       :



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           return None


class     _A       (	unittest.TestCase    ):
       UpperCamelCase__  :    List[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 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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



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


           '''simple docstring'''

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



       @require_torch
       @slow
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           from transformers import BertModel

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

           with TemporaryDirectory() as bert_save_dir:
               __a						       =				BertModel(BertConfig(vocab_size=len(__lowerCAmelCase)))
               model.save_pretrained(__lowerCAmelCase)
               self._test_export(__lowerCAmelCase     ,		'''pt'''     ,		12     ,		__lowerCAmelCase)



       @require_tf
       @slow
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

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


           '''simple docstring'''

           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
               __a						       =				self._test_export(__lowerCAmelCase     ,		'''pt'''     ,		12     ,		**__lowerCAmelCase)
               __a						       =				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							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Dict=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           try:
               # Compute path
               with TemporaryDirectory() as tempdir:
                   __a						       =				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							:     List[Any]):


           '''simple docstring'''

           from transformers import BertModel

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



       @require_tf
       @require_tokenizers
       @slow
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           from transformers import TFBertModel

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



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				FeatureExtractionPipeline(__lowerCAmelCase     ,		__lowerCAmelCase)

           __a						       =				['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
           __a						       =				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							:     Tuple):


           '''simple docstring'''

           __a						       =				['''input_ids''', '''attention_mask''', '''token_type_ids''']
           __a						       =				{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
           __a						       =				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						       =				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							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				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 json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

