
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowerCamelCase__    :		Optional[Any]          =   logging.getLogger(__name__)

def 		UpperCamelCase	(      lowercase_=2 , lowercase_=3 , lowercase_=16 , lowercase_ = 10 , lowercase_ = 2   )		->	Tuple:


  '''simple docstring'''






  def get_dataset(lowercase_   ):
    lowercase__  :   List[str]    		=      torch.randn(batch_size * n_batches , 1   )
    return TensorDataset(lowercase_ , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1   )   )

  lowercase__  :   str    		=      get_dataset(lowercase_   )
  lowercase__  :   Union[str, Any]    		=      get_dataset(lowercase_   )
  lowercase__  :   Union[str, Any]    		=      DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4   )
  lowercase__  :   Dict    		=      DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4   )
  return (train_dataloader, valid_dataloader)

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_=None   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      []
  for epoch in range(lowercase_   ):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowercase__  :   Union[str, Any]    		=      batch
      lowercase__  :   Dict    		=      model(lowercase_   )
      lowercase__  :   Optional[Any]    		=      torch.nn.functional.mse_loss(lowercase_ , lowercase_   )
      accelerator.backward(lowercase_   )
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()   )  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands



class       _snake_case   (	nn.Module							):


 def __init__(					self):


   '''simple docstring'''




   super().__init__()
   lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.randn(1))
   lowercase__  :   Dict    		=      nn.Parameter(torch.randn(1))



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return x * self.a + self.b



class       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self):


   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     lowercase__  :   Union[str, Any]    		=      DummyModel()
     lowercase__  :   Optional[int]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   int    		=      dummy_dataloaders()
     lowercase__  :   str    		=      ProjectConfiguration(total_limit=1					,  project_dir=SCREAMING_SNAKE_CASE_					,  automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_)
     # Train baseline
     lowercase__  :   Any    		=      Accelerator(project_config=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Tuple    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save initial
     accelerator.save_state()

     # Save second state
     accelerator.save_state()
     self.assertEqual(len(os.listdir(accelerator.project_dir))					,  1)


 def      lowercase__     (					self):


   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     lowercase__  :   List[Any]    		=      DummyModel()
     lowercase__  :   List[Any]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   List[str]    		=      dummy_dataloaders()
     # Train baseline
     lowercase__  :   str    		=      Accelerator()
     lowercase__  :   Tuple    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save initial
     lowercase__  :   Optional[int]    		=      os.path.join(SCREAMING_SNAKE_CASE_					,  """initial""")
     accelerator.save_state(SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   List[Any]    		=      model.a.item(), model.b.item()
     lowercase__  :   Any    		=      optimizer.state_dict()
     lowercase__  :   Tuple    		=      train(3					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   str    		=      model.a.item(), model.b.item()
     lowercase__  :   Any    		=      optimizer.state_dict()

     # Train partially
     set_seed(42)
     lowercase__  :   List[str]    		=      DummyModel()
     lowercase__  :   Optional[int]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   Tuple    		=      dummy_dataloaders()
     lowercase__  :   Tuple    		=      Accelerator()
     lowercase__  :   Tuple    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     accelerator.load_state(SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   Tuple    		=      model.a.item(), model.b.item()
     lowercase__  :   Dict    		=      optimizer.state_dict()
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

     lowercase__  :   int    		=      train(2					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save everything
     lowercase__  :   List[Any]    		=      os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoint""")
     accelerator.save_state(SCREAMING_SNAKE_CASE_)

     # Load everything back in and make sure all states work
     accelerator.load_state(SCREAMING_SNAKE_CASE_)
     test_rands += train(1					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   Optional[int]    		=      model.a.item(), model.b.item()
     lowercase__  :   Union[str, Any]    		=      optimizer.state_dict()
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     lowercase__  :   Optional[Any]    		=      DummyModel()
     lowercase__  :   Optional[int]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   int    		=      dummy_dataloaders()
     lowercase__  :   int    		=      ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_)

     # Train baseline
     lowercase__  :   Union[str, Any]    		=      Accelerator(project_dir=SCREAMING_SNAKE_CASE_					,  project_config=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[int]    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save initial
     accelerator.save_state()
     (lowercase__)  :   Optional[Any]    		=      model.a.item(), model.b.item()
     lowercase__  :   int    		=      optimizer.state_dict()
     lowercase__  :   Optional[int]    		=      train(3					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   List[str]    		=      model.a.item(), model.b.item()
     lowercase__  :   Tuple    		=      optimizer.state_dict()

     # Train partially
     set_seed(42)
     lowercase__  :   Any    		=      DummyModel()
     lowercase__  :   List[Any]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   Tuple    		=      dummy_dataloaders()
     lowercase__  :   Tuple    		=      ProjectConfiguration(iteration=1					,  automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      Accelerator(project_dir=SCREAMING_SNAKE_CASE_					,  project_config=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_0"""))
     (lowercase__)  :   Union[str, Any]    		=      model.a.item(), model.b.item()
     lowercase__  :   str    		=      optimizer.state_dict()
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

     lowercase__  :   Union[str, Any]    		=      train(2					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save everything
     accelerator.save_state()

     # Load everything back in and make sure all states work
     accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_1"""))
     test_rands += train(1					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     (lowercase__)  :   str    		=      model.a.item(), model.b.item()
     lowercase__  :   List[Any]    		=      optimizer.state_dict()
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      torch.tensor([1, 2, 3])
   lowercase__  :   Dict    		=      torch.tensor([2, 3, 4])
   lowercase__  :   List[str]    		=      DummyModel()
   lowercase__  :   List[Any]    		=      torch.optim.Adam(net.parameters())
   lowercase__  :   str    		=      Accelerator()
   with self.assertRaises(SCREAMING_SNAKE_CASE_) as ve:
     accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      str(ve.exception)
   self.assertTrue("""Item at index 0""" in message)
   self.assertTrue("""Item at index 1""" in message)
   self.assertFalse("""Item at index 2""" in message)
   self.assertFalse("""Item at index 3""" in message)


 def      lowercase__     (					self):


   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     lowercase__  :   Any    		=      DummyModel()
     lowercase__  :   Optional[int]    		=      torch.optim.Adam(params=model.parameters()					,  lr=1E-3)
     lowercase__  :   Dict    		=      torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE_					,  step_size=1					,  gamma=0.9_9)
     lowercase__  :   Union[str, Any]    		=      dummy_dataloaders()
     lowercase__  :   Any    		=      ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_)
     # Train baseline
     lowercase__  :   Optional[Any]    		=      Accelerator(project_dir=SCREAMING_SNAKE_CASE_					,  project_config=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Any    		=      accelerator.prepare(
         SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     # Save initial
     accelerator.save_state()
     lowercase__  :   Dict    		=      scheduler.state_dict()
     train(3					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
     self.assertNotEqual(SCREAMING_SNAKE_CASE_					,  scheduler.state_dict())

     # Load everything back in and make sure all states work
     accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_0"""))
     self.assertEqual(SCREAMING_SNAKE_CASE_					,  scheduler.state_dict())


 def      lowercase__     (					self):


   '''simple docstring'''




   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     lowercase__  :   Any    		=      DummyModel()
     lowercase__  :   Dict    		=      ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_					,  total_limit=2)
     # Train baseline
     lowercase__  :   Any    		=      Accelerator(project_dir=SCREAMING_SNAKE_CASE_					,  project_config=SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      accelerator.prepare(SCREAMING_SNAKE_CASE_)
     # Save 3 states:
     for _ in range(11):
       accelerator.save_state()
     self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_0""")))
     self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_9""")))
     self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """checkpoints"""					,  """checkpoint_10""")))



 @require_cuda
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
   execute_subprocess_async(SCREAMING_SNAKE_CASE_					,  env=os.environ.copy())


if __name__ == "__main__":
     lowerCamelCase__    :		Union[str, Any]          =   """/tmp/accelerate/state_checkpointing"""
     lowerCamelCase__    :		Tuple          =   DummyModel()
     lowerCamelCase__    :		int          =   torch.optim.Adam(params=model.parameters(), lr=1E-3)
     lowerCamelCase__    :		str          =   torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
     lowerCamelCase__    :		Any          =   dummy_dataloaders()
     lowerCamelCase__    :		Optional[Any]          =   ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     lowerCamelCase__    :		Any          =   Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
     if accelerator.process_index == 0:
          if os.path.exists(savedir):
               shutil.rmtree(savedir)
          os.makedirs(savedir)
     lowerCamelCase__    :		int          =   accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     lowerCamelCase__    :		int          =   accelerator.prepare(model, optimizer)
     train(3, model, train_dataloader, optimizer, accelerator, scheduler)
     # Check that the intial optimizer is loaded on the GPU
     for group in optimizer.param_groups:
          lowerCamelCase__    :		List[str]          =   group["""params"""][0].device
          break
     assert param_device.type == accelerator.device.type
     lowerCamelCase__    :		Optional[int]          =   model.cpu()
     accelerator.wait_for_everyone()
     accelerator.save_state()
     accelerator.wait_for_everyone()

     # Check CPU state
     accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
     for group in optimizer.param_groups:
          lowerCamelCase__    :		Dict          =   group["""params"""][0].device
          break
     assert (
         param_device.type == torch.device("""cpu""").type
     ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

     # Check device state
     model.to(accelerator.device)
     accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
     for group in optimizer.param_groups:
          lowerCamelCase__    :		Dict          =   group["""params"""][0].device
          break
     assert (
         param_device.type == accelerator.device.type
     ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

     # Check error
     with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
          accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
     accelerator.wait_for_everyone()
     if accelerator.process_index == 0:
          shutil.rmtree(savedir)
     accelerator.wait_for_everyone()






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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   :


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return None



class       _snake_case   (	unittest.TestCase							):
 __lowerCAmelCase  :     Optional[Any]        =			[
     # (model_name, model_kwargs)
     ('bert-base-cased', {}),
     ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
 ]


 @require_tf
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




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


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




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


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Union[str, Any]    		=      ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
   with NamedTemporaryFile(mode="""w+t""") as vocab_file:
     vocab_file.write("""\n""".join(SCREAMING_SNAKE_CASE_))
     vocab_file.flush()
     lowercase__  :   Optional[int]    		=      BertTokenizerFast(vocab_file.name)

   with TemporaryDirectory() as bert_save_dir:
     lowercase__  :   Union[str, Any]    		=      BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE_)))
     model.save_pretrained(SCREAMING_SNAKE_CASE_)
     self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  SCREAMING_SNAKE_CASE_)


 @require_tf
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Any    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """tf"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[Any]    		=      quantize(Path(SCREAMING_SNAKE_CASE_))

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


 @require_torch
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
     lowercase__  :   Tuple    		=      self._test_export(SCREAMING_SNAKE_CASE_					,  """pt"""					,  12					,  **SCREAMING_SNAKE_CASE_)
     lowercase__  :   Dict    		=      quantize(SCREAMING_SNAKE_CASE_)

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   try:
     # Compute path
     with TemporaryDirectory() as tempdir:
       lowercase__  :   List[Any]    		=      Path(SCREAMING_SNAKE_CASE_).joinpath("""model.onnx""")

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

     # Export
     convert(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)

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


 @require_torch
 @require_tokenizers
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import BertModel

   lowercase__  :   Dict    		=      BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Union[str, Any]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """pt""")


 @require_tf
 @require_tokenizers
 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   from transformers import TFBertModel

   lowercase__  :   Union[str, Any]    		=      TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""))
   lowercase__  :   Optional[int]    		=      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""")
   self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  """tf""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      FeatureExtractionPipeline(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
   lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   List[str]    		=      infer_shapes(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

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


   '''simple docstring'''




   lowercase__  :   Any    		=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
   lowercase__  :   Tuple    		=      {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
   lowercase__		,    lowercase__  :   Optional[Any]    		=      ensure_valid_input(FuncContiguousArgs()					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

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

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

   # Parameter should be reordered according to their respective place in the function:
   # (input_ids, token_type_ids, attention_mask)
   self.assertEqual(SCREAMING_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)
   lowercase__		,    lowercase__  :   Any    		=      ensure_valid_input(FuncNonContiguousArgs()					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

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


   '''simple docstring'''




   lowercase__  :   List[str]    		=      generate_identified_filename(Path("""/home/something/my_fake_model.onnx""")					,  """-test""")
   self.assertEqual("""/home/something/my_fake_model-test.onnx"""					,  generated.as_posix())






