
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCamelCase__    :		Dict          =   pytest.mark.integration

@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""]   )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Optional[Any]:


  '''simple docstring'''






  inspect_dataset(a_ , a_   )
  lowercase__  :   Any    		=      path + '''.py'''
  assert script_name in os.listdir(a_   )
  assert "__pycache__" not in os.listdir(a_   )

@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""   )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
@pytest.mark.parametrize("""path""" , ["""accuracy"""]   )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Optional[Any]:


  '''simple docstring'''






  inspect_metric(a_ , a_   )
  lowercase__  :   Dict    		=      path + '''.py'''
  assert script_name in os.listdir(a_   )
  assert "__pycache__" not in os.listdir(a_   )

@pytest.mark.parametrize(
    """path, config_name, expected_splits""" , [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      get_dataset_config_info(a_ , config_name=a_   )
  assert info.config_name == config_name
  assert list(info.splits.keys()   ) == expected_splits

@pytest.mark.parametrize(
    """path, config_name, expected_exception""" , [
        ("""paws""", None, ValueError),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  with pytest.raises(a_   ):
    get_dataset_config_info(a_ , config_name=a_   )

@pytest.mark.parametrize(
    """path, expected""" , [
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      get_dataset_config_names(a_   )
  assert expected in config_names

@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config""" , [
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Dict    		=      get_dataset_infos(a_   )
  assert list(infos.keys()   ) == expected_configs
  lowercase__  :   str    		=      expected_configs[0]
  assert expected_config in infos
  lowercase__  :   Optional[Any]    		=      infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    """path, expected_config, expected_splits""" , [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   int    		=      get_dataset_infos(a_   )
  assert expected_config in infos
  lowercase__  :   Any    		=      infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception""" , [
        ("""paws""", None, ValueError),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Optional[int]:


  '''simple docstring'''






  with pytest.raises(a_   ):
    get_dataset_split_names(a_ , config_name=a_   )






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())






