


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     __UpperCAmelCase							:


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        return None







class     __UpperCAmelCase							:


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A    ):
        return None







class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      [
         # (model_name, model_kwargs)
         ("""bert-base-cased""", {}),
         ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
     ]


     @require_tf
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           self._test_export(__A  ,       """tf"""  ,       12  ,       **__A    )


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           self._test_export(__A  ,       """pt"""  ,       12  ,       **__A    )


     @require_torch
     @slow
     def 	snake_case_  (	self    ):
        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(__A    )    )
           vocab_file.flush()
           __a					=  BertTokenizerFast(vocab_file.name    )

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


     @require_tf
     @slow
     def 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __a					=  self._test_export(__A  ,       """tf"""  ,       12  ,       **__A    )
           __a					=  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 	snake_case_  (	self    ):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           __a					=  self._test_export(__A  ,       """pt"""  ,       12  ,       **__A    )
           __a					=  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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       **__A    ):
        try:
           # Compute path
           with TemporaryDirectory() as tempdir:
              __a					=  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 	snake_case_  (	self    ):
        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(__A  ,       __A  ,       """pt"""    )


     @require_tf
     @require_tokenizers
     @slow
     def 	snake_case_  (	self    ):
        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(__A  ,       __A  ,       """tf"""    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  FeatureExtractionPipeline(__A  ,       __A    )

        __a					=  ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
        __a	,    __a	,    __a	,    __a					=  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 	snake_case_  (	self    ):
        __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	,    __a					=  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)
        __a	,    __a					=  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 	snake_case_  (	self    ):
        __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'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




