


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

_lowercase							:   Any						=						collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowercase							:   Optional[Any]						=						"https://storage.googleapis.com/cvdf-datasets/mnist/"





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Optional[int]:





	"""simple docstring"""







	A             =					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 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> List[Any]:





	"""simple docstring"""







	print("""Extracting"""				,       f.name    )
	with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
		A             =					_readaa(UpperCamelCase__    )
		if magic != 20_51:
			raise ValueError(
			    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)    )
		A             =					_readaa(UpperCamelCase__    )
		A             =					_readaa(UpperCamelCase__    )
		A             =					_readaa(UpperCamelCase__    )
		A             =					bytestream.read(rows * cols * num_images    )
		A             =					numpy.frombuffer(UpperCamelCase__				,       dtype=numpy.uinta    )
		A             =					data.reshape(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       1    )
		return data





@deprecated(UpperCamelCase__				,       """Please use tf.one_hot on tensors."""    )
def 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  List[str]    )			-> int:





	"""simple docstring"""







	A             =					labels_dense.shape[0]
	A             =					numpy.arange(UpperCamelCase__    ) * num_classes
	A             =					numpy.zeros((num_labels, num_classes)    )
	A             =					1
	return labels_one_hot





@deprecated(UpperCamelCase__				,       """Please use tf.data to implement this functionality."""    )
def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Tuple=False				,       UpperCamelCase__:  Dict=10    )			-> Optional[Any]:





	"""simple docstring"""







	print("""Extracting"""				,       f.name    )
	with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
		A             =					_readaa(UpperCamelCase__    )
		if magic != 20_49:
			raise ValueError(
			    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)    )
		A             =					_readaa(UpperCamelCase__    )
		A             =					bytestream.read(UpperCamelCase__    )
		A             =					numpy.frombuffer(UpperCamelCase__				,       dtype=numpy.uinta    )
		if one_hot:
			return _dense_to_one_hot(UpperCamelCase__				,       UpperCamelCase__    )
		return labels






class    _UpperCamelCase     :

				"""simple docstring"""







				@deprecated(
				    a__					, """Please use alternatives such as official/mnist/_DataSet.py"""
				    """ from tensorflow/models."""					, )
				def __init__(    self					, a__					, a__					, a__=False					, a__=False					, a__=dtypes.floataa					, a__=True					, a__=None					, )						->					List[Any]:
					A       ,      A             =					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 )
					A             =					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:
						A             =					1_0000
						A             =					one_hot
					else:
						assert (
						    images.shape[0] == labels.shape[0]
						), f'images.shape: {images.shape} labels.shape: {labels.shape}'
						A             =					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
							A             =					images.reshape(
							    images.shape[0]					, images.shape[1] * images.shape[2] )
						if dtype == dtypes.floataa:
							# Convert from [0, 255] -> [0.0, 1.0].
							A             =					images.astype(numpy.floataa )
							A             =					numpy.multiply(a__					, 1.0 / 2_55.0 )
					A             =					images
					A             =					labels
					A             =					0
					A             =					0

				@property
				def   _UpperCAmelCase       (    self )						->					str:
					return self._images

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

				@property
				def   _UpperCAmelCase       (    self )						->					str:
					return self._num_examples

				@property
				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					return self._epochs_completed

				def   _UpperCAmelCase       (    self					, a__					, a__=False					, a__=True )						->					Union[str, Any]:
					if fake_data:
						A             =					[1] * 784
						A             =					[1] + [0] * 9 if self.one_hot else 0
						return (
						    [fake_image for _ in range(a__ )],
						    [fake_label for _ in range(a__ )],
						)
					A             =					self._index_in_epoch
					# Shuffle for the first epoch
					if self._epochs_completed == 0 and start == 0 and shuffle:
						A             =					numpy.arange(self._num_examples )
						numpy.random.shuffle(a__ )
						A             =					self.images[perma]
						A             =					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
						A             =					self._num_examples - start
						A             =					self._images[start : self._num_examples]
						A             =					self._labels[start : self._num_examples]
						# Shuffle the data
						if shuffle:
							A             =					numpy.arange(self._num_examples )
							numpy.random.shuffle(a__ )
							A             =					self.images[perm]
							A             =					self.labels[perm]
						# Start next epoch
						A             =					0
						A             =					batch_size - rest_num_examples
						A             =					self._index_in_epoch
						A             =					self._images[start:end]
						A             =					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
						A             =					self._index_in_epoch
						return self._images[start:end], self._labels[start:end]





@deprecated(UpperCamelCase__				,       """Please write your own downloading logic."""    )
def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[Any]    )			-> List[Any]:





	"""simple docstring"""







	if not gfile.Exists(UpperCamelCase__    ):
		gfile.MakeDirs(UpperCamelCase__    )
	A             =					os.path.join(UpperCamelCase__				,       UpperCamelCase__    )
	if not gfile.Exists(UpperCamelCase__    ):
		urllib.request.urlretrieve(UpperCamelCase__				,       UpperCamelCase__    )  # noqa: S310
		with gfile.GFile(UpperCamelCase__    ) as f:
			A             =					f.size()
		print("""Successfully downloaded"""				,       UpperCamelCase__				,       UpperCamelCase__				,       """bytes."""    )
	return filepath





@deprecated(
    UpperCamelCase__				,       """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""    )
def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  Any=False				,       UpperCamelCase__:  str=False				,       UpperCamelCase__:  Any=dtypes.floataa				,       UpperCamelCase__:  Optional[Any]=True				,       UpperCamelCase__:  List[Any]=50_00				,       UpperCamelCase__:  Optional[int]=None				,       UpperCamelCase__:  Optional[Any]=DEFAULT_SOURCE_URL				,       )			-> Optional[Any]:





	"""simple docstring"""







	if fake_data:

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

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

	if not source_url:  # empty string check
		A             =					DEFAULT_SOURCE_URL

	A             =					"""train-images-idx3-ubyte.gz"""
	A             =					"""train-labels-idx1-ubyte.gz"""
	A             =					"""t10k-images-idx3-ubyte.gz"""
	A             =					"""t10k-labels-idx1-ubyte.gz"""

	A             =					_maybe_download(
	    UpperCamelCase__				,       UpperCamelCase__				,       source_url + train_images_file    )
	with gfile.Open(UpperCamelCase__				,       """rb"""    ) as f:
		A             =					_extract_images(UpperCamelCase__    )

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

	A             =					_maybe_download(
	    UpperCamelCase__				,       UpperCamelCase__				,       source_url + test_images_file    )
	with gfile.Open(UpperCamelCase__				,       """rb"""    ) as f:
		A             =					_extract_images(UpperCamelCase__    )

	A             =					_maybe_download(
	    UpperCamelCase__				,       UpperCamelCase__				,       source_url + test_labels_file    )
	with gfile.Open(UpperCamelCase__				,       """rb"""    ) as f:
		A             =					_extract_labels(UpperCamelCase__				,       one_hot=UpperCamelCase__    )

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

	A             =					train_images[:validation_size]
	A             =					train_labels[:validation_size]
	A             =					train_images[validation_size:]
	A             =					train_labels[validation_size:]

	A             =					{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

	A             =					_DataSet(UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__    )
	A             =					_DataSet(UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__    )
	A             =					_DataSet(UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__    )

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







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase__    )
	m.model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__    )
	m.half()
	m.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   str						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
				parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
				parser.add_argument(
				    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



