
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      :
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       return None
class 			UpperCamelCase      :
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       return None
class 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			[
         # (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				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(snake_case__  ,     "tf"  ,     12  ,     **snake_case__				)
     @require_torch
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




       """simple docstring"""


       from transformers import BertModel

       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["[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()
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = BertTokenizerFast(vocab_file.name				)

       with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE   :				Any							    = self._test_export(snake_case__  ,     "tf"  ,     12  ,     **snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE   :				str							    = self._test_export(snake_case__  ,     "pt"  ,     12  ,     **snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       try:
         # Compute path
         with TemporaryDirectory() as tempdir:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers import BertModel

       _SCREAMING_SNAKE_CASE   :				str							    = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
       self._test_infer_dynamic_axis(snake_case__  ,     snake_case__  ,     "pt"				)
     @require_tf
     @require_tokenizers
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       from transformers import TFBertModel

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"				)				)
       _SCREAMING_SNAKE_CASE   :				str							    = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"				)
       self._test_infer_dynamic_axis(snake_case__  ,     snake_case__  ,     "tf"				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FeatureExtractionPipeline(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = ["input_ids", "attention_mask", "token_type_ids"]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
       _SCREAMING_SNAKE_CASE   :				Tuple							    = 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)
       _SCREAMING_SNAKE_CASE   :				Dict							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowercase_       :						Tuple     					=	[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							int=None,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Any=None				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = True
  while ask_again:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = input(lowerCamelCase__				)
    try:
      if default is not None and len(lowerCamelCase__				) == 0:
        return default
      return convert_value(lowerCamelCase__				) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict=[],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							str=0				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				int							    = BulletMenu(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = menu.run(default_choice=lowerCamelCase__				)
  return convert_value(lowerCamelCase__				) if convert_value is not None else result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = int(lowerCamelCase__				)
  return DynamoBackend(DYNAMO_BACKENDS[value]				).value





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = int(lowerCamelCase__				)
  return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(lowerCamelCase__				)
  return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  return {"yes": True, "no": False}[value.lower()]





class 			UpperCamelCase      (							argparse.RawDescriptionHelpFormatter  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = super()._format_usage(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = usage.replace("<command> [<args>] "  ,     ""				)
       return usage



