


"""simple docstring"""


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__lowerCamelCase      :str	    =		collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__lowerCamelCase      :int	    =		'https://storage.googleapis.com/cvdf-datasets/mnist/'




def   snake_case		(     UpperCamelCase__       :						int    )   ->      Any:
      lowerCamelCase					:					Dict        =	numpy.dtype(numpy.uintaa    ).newbyteorder(""">"""    )
      return numpy.frombuffer(bytestream.read(4    )			,				dtype=UpperCamelCase__    )[0]




@deprecated(UpperCamelCase__			,				"""Please use tf.data to implement this functionality."""    )
def   snake_case		(     UpperCamelCase__       :						List[str]    )   ->      Any:
      print("""Extracting"""			,				f.name    )
      with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
            lowerCamelCase					:					Optional[Any]        =	_readaa(UpperCamelCase__    )
            if magic != 2051:
                  raise ValueError(
                      """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)    )
            lowerCamelCase					:					List[Any]        =	_readaa(UpperCamelCase__    )
            lowerCamelCase					:					int        =	_readaa(UpperCamelCase__    )
            lowerCamelCase					:					Optional[Any]        =	_readaa(UpperCamelCase__    )
            lowerCamelCase					:					Dict        =	bytestream.read(rows * cols * num_images    )
            lowerCamelCase					:					Dict        =	numpy.frombuffer(UpperCamelCase__			,				dtype=numpy.uinta    )
            lowerCamelCase					:					Optional[int]        =	data.reshape(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				1    )
            return data




@deprecated(UpperCamelCase__			,				"""Please use tf.one_hot on tensors."""    )
def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]    )   ->      Union[str, Any]:
      lowerCamelCase					:					List[Any]        =	labels_dense.shape[0]
      lowerCamelCase					:					Union[str, Any]        =	numpy.arange(UpperCamelCase__    ) * num_classes
      lowerCamelCase					:					List[Any]        =	numpy.zeros((num_labels, num_classes)    )
      lowerCamelCase					:					Tuple        =	1
      return labels_one_hot




@deprecated(UpperCamelCase__			,				"""Please use tf.data to implement this functionality."""    )
def   snake_case		(     UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Dict=False			,				UpperCamelCase__       :						List[str]=10    )   ->      str:
      print("""Extracting"""			,				f.name    )
      with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
            lowerCamelCase					:					Dict        =	_readaa(UpperCamelCase__    )
            if magic != 2049:
                  raise ValueError(
                      """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)    )
            lowerCamelCase					:					str        =	_readaa(UpperCamelCase__    )
            lowerCamelCase					:					Any        =	bytestream.read(UpperCamelCase__    )
            lowerCamelCase					:					Tuple        =	numpy.frombuffer(UpperCamelCase__			,				dtype=numpy.uinta    )
            if one_hot:
                  return _dense_to_one_hot(UpperCamelCase__			,				UpperCamelCase__    )
            return labels


class 			A__	:


     """simple docstring"""

     @deprecated(
         __a     ,		"""Please use alternatives such as official/mnist/_DataSet.py"""
         """ from tensorflow/models."""     ,		)
     def __init__(	self:						Tuple     ,		__a:						Tuple     ,		__a:						Dict     ,		__a:						Tuple=False     ,		__a:						int=False     ,		__a:						Optional[int]=dtypes.floataa     ,		__a:						Tuple=True     ,		__a:						int=None     ,		)->    Any:
           lowerCamelCase					:					Dict        =	random_seed.get_seed(__a	)
           # If op level seed is not set, use whatever graph level seed is returned
           numpy.random.seed(seeda if seed is None else seeda	)
           lowerCamelCase					:					str        =	dtypes.as_dtype(__a	).base_dtype
           if dtype not in (dtypes.uinta, dtypes.floataa):
                 raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype	)
           if fake_data:
                 lowerCamelCase					:					Optional[Any]        =	10_000
                 lowerCamelCase					:					int        =	one_hot
           else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
                 lowerCamelCase					:					Union[str, Any]        =	images.shape[0]

                 # Convert shape from [num examples, rows, columns, depth]
                 # to [num examples, rows*columns] (assuming depth == 1)
                 if reshape:
                       assert images.shape[3] == 1
                       lowerCamelCase					:					List[str]        =	images.reshape(
                           images.shape[0]     ,		images.shape[1] * images.shape[2]	)
                 if dtype == dtypes.floataa:
                       # Convert from [0, 255] -> [0.0, 1.0].
                       lowerCamelCase					:					Optional[Any]        =	images.astype(numpy.floataa	)
                       lowerCamelCase					:					List[str]        =	numpy.multiply(__a     ,		1.0 / 255.0	)
           lowerCamelCase					:					Any        =	images
           lowerCamelCase					:					List[str]        =	labels
           lowerCamelCase					:					Dict        =	0
           lowerCamelCase					:					Union[str, Any]        =	0

     @property
     def 		a__						(	self:						Tuple	)->    List[Any]:
           return self._images

     @property
     def 		a__						(	self:						Optional[int]	)->    int:
           return self._labels

     @property
     def 		a__						(	self:						List[Any]	)->    Optional[Any]:
           return self._num_examples

     @property
     def 		a__						(	self:						Optional[int]	)->    Dict:
           return self._epochs_completed







     def 		a__						(	self:						Any     ,		__a:						str     ,		__a:						Optional[int]=False     ,		__a:						str=True	)->    Optional[int]:
           if fake_data:
                 lowerCamelCase					:					List[str]        =	[1] * 784
                 lowerCamelCase					:					str        =	[1] + [0] * 9 if self.one_hot else 0
                 return (
                     [fake_image for _ in range(__a	)],
                     [fake_label for _ in range(__a	)],
                 )
           lowerCamelCase					:					Dict        =	self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
                 lowerCamelCase					:					Union[str, Any]        =	numpy.arange(self._num_examples	)
                 numpy.random.shuffle(__a	)
                 lowerCamelCase					:					Any        =	self.images[perma]
                 lowerCamelCase					:					Optional[int]        =	self.labels[perma]
           # Go to the next epoch
           if start + batch_size > self._num_examples:
                 # Finished epoch
                 self._epochs_completed += 1
                 # Get the rest examples in this epoch
                 lowerCamelCase					:					Union[str, Any]        =	self._num_examples - start
                 lowerCamelCase					:					Tuple        =	self._images[start : self._num_examples]
                 lowerCamelCase					:					int        =	self._labels[start : self._num_examples]
                 # Shuffle the data
                 if shuffle:
                       lowerCamelCase					:					Optional[int]        =	numpy.arange(self._num_examples	)
                       numpy.random.shuffle(__a	)
                       lowerCamelCase					:					Dict        =	self.images[perm]
                       lowerCamelCase					:					Optional[int]        =	self.labels[perm]
                 # Start next epoch
                 lowerCamelCase					:					str        =	0
                 lowerCamelCase					:					Union[str, Any]        =	batch_size - rest_num_examples
                 lowerCamelCase					:					str        =	self._index_in_epoch
                 lowerCamelCase					:					List[Any]        =	self._images[start:end]
                 lowerCamelCase					:					Union[str, Any]        =	self._labels[start:end]
                 return (
                     numpy.concatenate((images_rest_part, images_new_part)     ,		axis=0	),
                     numpy.concatenate((labels_rest_part, labels_new_part)     ,		axis=0	),
                 )
           else:
                 self._index_in_epoch += batch_size
                 lowerCamelCase					:					List[Any]        =	self._index_in_epoch
                 return self._images[start:end], self._labels[start:end]




@deprecated(UpperCamelCase__			,				"""Please write your own downloading logic."""    )
def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						Dict    )   ->      Any:
      if not gfile.Exists(UpperCamelCase__    ):
            gfile.MakeDirs(UpperCamelCase__    )
      lowerCamelCase					:					Optional[Any]        =	os.path.join(UpperCamelCase__			,				UpperCamelCase__    )
      if not gfile.Exists(UpperCamelCase__    ):
            urllib.request.urlretrieve(UpperCamelCase__			,				UpperCamelCase__    )  # noqa: S310
            with gfile.GFile(UpperCamelCase__    ) as f:
                  lowerCamelCase					:					str        =	f.size()
            print("""Successfully downloaded"""			,				UpperCamelCase__			,				UpperCamelCase__			,				"""bytes."""    )
      return filepath






@deprecated(
    UpperCamelCase__			,				"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""    )
def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Union[str, Any]=False			,				UpperCamelCase__       :						List[Any]=False			,				UpperCamelCase__       :						str=dtypes.floataa			,				UpperCamelCase__       :						str=True			,				UpperCamelCase__       :						str=5000			,				UpperCamelCase__       :						Tuple=None			,				UpperCamelCase__       :						str=DEFAULT_SOURCE_URL			,				)   ->      Tuple:
      if fake_data:

            def fake():
                  return _DataSet(
                      []			,				[]			,				fake_data=UpperCamelCase__			,				one_hot=UpperCamelCase__			,				dtype=UpperCamelCase__			,				seed=UpperCamelCase__    )

            lowerCamelCase					:					Tuple        =	fake()
            lowerCamelCase					:					List[str]        =	fake()
            lowerCamelCase					:					str        =	fake()
            return _Datasets(train=UpperCamelCase__			,				validation=UpperCamelCase__			,				test=UpperCamelCase__    )

      if not source_url:  # empty string check
            lowerCamelCase					:					List[str]        =	DEFAULT_SOURCE_URL

      lowerCamelCase					:					Any        =	"""train-images-idx3-ubyte.gz"""
      lowerCamelCase					:					Optional[int]        =	"""train-labels-idx1-ubyte.gz"""
      lowerCamelCase					:					Optional[Any]        =	"""t10k-images-idx3-ubyte.gz"""
      lowerCamelCase					:					Any        =	"""t10k-labels-idx1-ubyte.gz"""

      lowerCamelCase					:					Any        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + train_images_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					List[Any]        =	_extract_images(UpperCamelCase__    )

      lowerCamelCase					:					Tuple        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + train_labels_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					int        =	_extract_labels(UpperCamelCase__			,				one_hot=UpperCamelCase__    )

      lowerCamelCase					:					Optional[Any]        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + test_images_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					Optional[int]        =	_extract_images(UpperCamelCase__    )

      lowerCamelCase					:					List[Any]        =	_maybe_download(
          UpperCamelCase__			,				UpperCamelCase__			,				source_url + test_labels_file    )
      with gfile.Open(UpperCamelCase__			,				"""rb"""    ) as f:
            lowerCamelCase					:					Dict        =	_extract_labels(UpperCamelCase__			,				one_hot=UpperCamelCase__    )

      if not 0 <= validation_size <= len(UpperCamelCase__    ):
            lowerCamelCase					:					Tuple        =	(
                """Validation size should be between 0 and """
                F'{len(UpperCamelCase__    )}. Received: {validation_size}.'
            )
            raise ValueError(UpperCamelCase__    )

      lowerCamelCase					:					Optional[Any]        =	train_images[:validation_size]
      lowerCamelCase					:					Tuple        =	train_labels[:validation_size]
      lowerCamelCase					:					Optional[int]        =	train_images[validation_size:]
      lowerCamelCase					:					Optional[int]        =	train_labels[validation_size:]

      lowerCamelCase					:					Union[str, Any]        =	{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

      lowerCamelCase					:					Optional[int]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )
      lowerCamelCase					:					List[Any]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )
      lowerCamelCase					:					Union[str, Any]        =	_DataSet(UpperCamelCase__			,				UpperCamelCase__			,				**UpperCamelCase__    )

      return _Datasets(train=UpperCamelCase__			,				validation=UpperCamelCase__			,				test=UpperCamelCase__    )






"""simple docstring"""


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 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple     ,		__a:						Optional[int]	)->    List[str]:
           return None


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						str     ,		__a:						str     ,		__a:						str	)->    Tuple:
           return None


class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					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 		a__						(	self:						Optional[Any]	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						str	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	["""[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()
                 lowerCamelCase					:					Dict        =	BertTokenizerFast(vocab_file.name	)

           with TemporaryDirectory() as bert_save_dir:
                 lowerCamelCase					:					List[str]        =	BertModel(BertConfig(vocab_size=len(__a	)	)	)
                 model.save_pretrained(__a	)
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		__a	)

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Optional[int]        =	self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Tuple        =	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 		a__						(	self:						Any	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Any        =	self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Dict        =	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 		a__						(	self:						List[Any]     ,		__a:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[int]	)->    Any:
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCamelCase					:					Optional[Any]        =	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 		a__						(	self:						Tuple	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					List[Any]        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""pt"""	)

     @require_tf
     @require_tokenizers
     @slow
     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           from transformers import TFBertModel

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

     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						str	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	FeatureExtractionPipeline(__a     ,		__a	)

           lowerCamelCase					:					List[str]        =	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	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 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowerCamelCase					:					str        =	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	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)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	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 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""	)     ,		"""-test"""	)
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()	)




