from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
   import tensorflow as tf


__lowerCamelCase    :				str							     =	logging.get_logger(__name__)







@dataclass
class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							[
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]




   def __init__(					self    :       str ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






     for deprecated_arg in self.deprecated_args:
       if deprecated_arg in kwargs:
         SCREAMING_SNAKE_CASE__    							=   deprecated_arg[3:]
         SCREAMING_SNAKE_CASE__    							=   not kwargs.pop(_lowercase   )
         logger.warning(
             f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
             f""" {positive_arg}={kwargs[positive_arg]}"""   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""tpu_name""" ,			self.tpu_name   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""device_idx""" ,			self.device_idx   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""eager_mode""" ,			self.eager_mode   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""use_xla""" ,			self.use_xla   )
     super().__init__(**_lowercase   )

   lowerCAmelCase_														=							field(
       default=lowerCamelCase_     ,	metadata={"help": "Name of TPU"}     ,	)
   lowerCAmelCase_														=							field(
       default=0     ,	metadata={"help": "CPU / GPU device index. Defaults to 0."}     ,	)
   lowerCAmelCase_														=							field(default=lowerCamelCase_     ,	metadata={"help": "Benchmark models in eager model."}	)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_     ,	metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }     ,	)




   @cached_property
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     SCREAMING_SNAKE_CASE__    							=   None
     if self.tpu:
       try:
         if self.tpu_name:
           SCREAMING_SNAKE_CASE__    							=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name   )
         else:
           SCREAMING_SNAKE_CASE__    							=   tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
         SCREAMING_SNAKE_CASE__    							=   None
     return tpu




   @cached_property
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     if self.is_tpu:
       tf.config.experimental_connect_to_cluster(self._setup_tpu   )
       tf.tpu.experimental.initialize_tpu_system(self._setup_tpu   )

       SCREAMING_SNAKE_CASE__    							=   tf.distribute.TPUStrategy(self._setup_tpu   )
     else:
       # currently no multi gpu is allowed
       if self.is_gpu:
         # TODO: Currently only single GPU is supported
         tf.config.set_visible_devices(self.gpu_list[self.device_idx] ,			"""GPU"""   )
         SCREAMING_SNAKE_CASE__    							=   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""   )
       else:
         tf.config.set_visible_devices([] ,			"""GPU"""   )  # disable GPU
         SCREAMING_SNAKE_CASE__    							=   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""   )

     return strategy




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     return self._setup_tpu is not None




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     return self._setup_strategy




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     return tf.config.list_physical_devices("""GPU"""   )




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     requires_backends(self ,			["""tf"""]   )
     if self.cuda:
       return len(self.gpu_list   )
     return 0




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.n_gpu > 0

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 __snake_case       :




   def 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







class __snake_case       (		unittest.TestCase	):
   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 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






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




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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




   @require_torch
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
     with NamedTemporaryFile(mode="""w+t"""   ) as vocab_file:
       vocab_file.write("""\n""".join(_lowercase   )   )
       vocab_file.flush()
       SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast(vocab_file.name   )

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




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(Path(_lowercase   )   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




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



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(_lowercase   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ).joinpath("""model.onnx"""   )

       # Remove folder if exists
       if path.parent.exists():
         path.parent.rmdir()

       # Export
       convert(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   )

       return path
     except Exception as e:
       self.fail(_lowercase   )




   @require_torch
   @require_tokenizers
   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""pt"""   )




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import TFBertModel

     SCREAMING_SNAKE_CASE__    							=   TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""tf"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   infer_shapes(_lowercase ,			_lowercase   )

     # Assert all variables are present
     self.assertEqual(len(_lowercase   ) ,			len(_lowercase   )   )
     self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
     self.assertSequenceEqual(variable_names[:3] ,			_lowercase   )
     self.assertSequenceEqual(variable_names[3:] ,			_lowercase   )

     # 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 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """attention_mask""", """token_type_ids"""]
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(_lowercase   ) ,			3   )

     # Should have exactly the same input names
     self.assertEqual(set(_lowercase   ) ,			set(_lowercase   )   )

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(_lowercase ,			(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__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncNonContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(_lowercase   ) ,			1   )
     self.assertEqual(len(_lowercase   ) ,			1   )

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0] ,			tokens["""input_ids"""]   )
     self.assertEqual(ordered_input_names[0] ,			"""input_ids"""   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""   ) ,			"""-test"""   )
     self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,			generated.as_posix()   )
