
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase						:      Tuple									= logging.get_logger(__name__)


lowercase						:      int									= {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'codegen'
	_A			=  {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(      self	:Union[str, Any]  ,     a	:str=5_0_4_0_0  ,     a	:Dict=2_0_4_8  ,     a	:str=2_0_4_8  ,     a	:List[str]=4_0_9_6  ,     a	:List[Any]=2_8  ,     a	:List[Any]=1_6  ,     a	:int=6_4  ,     a	:Optional[int]=None  ,     a	:List[Any]="gelu_new"  ,     a	:Any=0.0  ,     a	:str=0.0  ,     a	:Optional[int]=0.0  ,     a	:Optional[int]=1E-5  ,     a	:Tuple=0.02  ,     a	:Optional[Any]=True  ,     a	:Optional[int]=5_0_2_5_6  ,     a	:Any=5_0_2_5_6  ,     a	:str=False  ,     **a	:Any  ,     )	->				Optional[Any]:
								__UpperCamelCase			:     Tuple         =      vocab_size
								__UpperCamelCase			:     str         =      n_ctx
								__UpperCamelCase			:     List[Any]         =      n_positions
								__UpperCamelCase			:     str         =      n_embd
								__UpperCamelCase			:     Optional[Any]         =      n_layer
								__UpperCamelCase			:     Tuple         =      n_head
								__UpperCamelCase			:     Dict         =      n_inner
								__UpperCamelCase			:     Union[str, Any]         =      rotary_dim
								__UpperCamelCase			:     List[str]         =      activation_function
								__UpperCamelCase			:     List[str]         =      resid_pdrop
								__UpperCamelCase			:     List[str]         =      embd_pdrop
								__UpperCamelCase			:     Any         =      attn_pdrop
								__UpperCamelCase			:     Optional[int]         =      layer_norm_epsilon
								__UpperCamelCase			:     int         =      initializer_range
								__UpperCamelCase			:     Tuple         =      use_cache

								__UpperCamelCase			:     Union[str, Any]         =      bos_token_id
								__UpperCamelCase			:     Dict         =      eos_token_id

								super().__init__(
								    bos_token_id=a  ,     eos_token_id=a  ,     tie_word_embeddings=a  ,     **a		)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Dict  ,     a	:PretrainedConfig  ,     a	:str = "default"  ,     a	:List[PatchingSpec] = None  ,     a	:bool = False  ,     )	->				Optional[Any]:
								super().__init__(a  ,     task=a  ,     patching_specs=a  ,     use_past=a		)
								if not getattr(self._config  ,     "pad_token_id"  ,     a		):
															# TODO: how to do that better?
															__UpperCamelCase			:     Tuple         =      0







	@property
	def      _lowerCamelCase       (      self	:int		)	->				Mapping[str, Mapping[int, str]]:
								__UpperCamelCase			:     int         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
								if self.use_past:
															self.fill_with_past_key_values_(a  ,     direction="inputs"		)
															__UpperCamelCase			:     Any         =      {0: "batch", 1: "past_sequence + sequence"}
								else:
															__UpperCamelCase			:     Tuple         =      {0: "batch", 1: "sequence"}

								return common_inputs







	@property
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				int:
								return self._config.n_layer







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				int:
								return self._config.n_head







	def      _lowerCamelCase       (      self	:str  ,     a	:PreTrainedTokenizer  ,     a	:int = -1  ,     a	:int = -1  ,     a	:bool = False  ,     a	:Optional[TensorType] = None  ,     )	->				Mapping[str, Any]:
								__UpperCamelCase			:     int         =      super(a  ,     self		).generate_dummy_inputs(
								    a  ,     batch_size=a  ,     seq_length=a  ,     is_pair=a  ,     framework=a		)

								# We need to order the input in the way they appears in the forward()
								__UpperCamelCase			:     str         =      OrderedDict({"input_ids": common_inputs["input_ids"]}		)

								# Need to add the past_keys
								if self.use_past:
															if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																						import torch

																						__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						__UpperCamelCase			:     Optional[Any]         =      seqlen + 2
																						__UpperCamelCase			:     List[Any]         =      (
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						__UpperCamelCase			:     Union[str, Any]         =      [
																						    (torch.zeros(a		), torch.zeros(a		)) for _ in range(self.num_layers		)
																						]

								__UpperCamelCase			:     List[Any]         =      common_inputs["attention_mask"]
								if self.use_past:
															__UpperCamelCase			:     int         =      ordered_inputs["attention_mask"].dtype
															__UpperCamelCase			:     Any         =      torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(a  ,     a  ,     dtype=a		)]  ,     dim=1		)

								return ordered_inputs







	@property
	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								return 1_3

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase						:      List[Any]									= 'https://storage.googleapis.com/cvdf-datasets/mnist/'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      numpy.dtype(numpy.uintaa).newbyteorder(">")
							return numpy.frombuffer(bytestream.read(4)		,						dtype=_lowerCamelCase)[0]




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     str         =      _readaa(_lowerCamelCase)
														if magic != 2_051:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name))
														__UpperCamelCase			:     List[str]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Optional[int]         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      bytestream.read(rows * cols * num_images)
														__UpperCamelCase			:     Optional[int]         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														__UpperCamelCase			:     Dict         =      data.reshape(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						1)
														return data




@deprecated(_lowerCamelCase		,						"Please use tf.one_hot on tensors.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      labels_dense.shape[0]
							__UpperCamelCase			:     str         =      numpy.arange(_lowerCamelCase) * num_classes
							__UpperCamelCase			:     str         =      numpy.zeros((num_labels, num_classes))
							__UpperCamelCase			:     Tuple         =      1
							return labels_one_hot




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






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     int         =      _readaa(_lowerCamelCase)
														if magic != 2_049:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name))
														__UpperCamelCase			:     Any         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      bytestream.read(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase		,						_lowerCamelCase)
														return labels




class 				lowerCamelCase__							:




	'''simple docstring'''




	@deprecated(
	    a  ,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."  ,     )
	def __init__(      self	:int  ,     a	:Any  ,     a	:List[str]  ,     a	:Union[str, Any]=False  ,     a	:List[Any]=False  ,     a	:Dict=dtypes.floataa  ,     a	:int=True  ,     a	:Optional[int]=None  ,     )	->				List[str]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      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		)
								__UpperCamelCase			:     Optional[Any]         =      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:
															__UpperCamelCase			:     str         =      1_0_0_0_0
															__UpperCamelCase			:     Optional[Any]         =      one_hot
								else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'images.shape: {images.shape} labels.shape: {labels.shape}'
															__UpperCamelCase			:     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
																						__UpperCamelCase			:     Union[str, Any]         =      images.reshape(
																						    images.shape[0]  ,     images.shape[1] * images.shape[2]		)
															if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						__UpperCamelCase			:     List[Any]         =      images.astype(numpy.floataa		)
																						__UpperCamelCase			:     Optional[Any]         =      numpy.multiply(a  ,     1.0 / 255.0		)
								__UpperCamelCase			:     Optional[Any]         =      images
								__UpperCamelCase			:     List[Any]         =      labels
								__UpperCamelCase			:     str         =      0
								__UpperCamelCase			:     Union[str, Any]         =      0







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								return self._images







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Dict:
								return self._labels







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								return self._num_examples







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
								return self._epochs_completed







	def      _lowerCamelCase       (      self	:Any  ,     a	:Optional[int]  ,     a	:Optional[int]=False  ,     a	:int=True		)	->				Optional[int]:
								if fake_data:
															__UpperCamelCase			:     Any         =      [1] * 7_8_4
															__UpperCamelCase			:     Union[str, Any]         =      [1] + [0] * 9 if self.one_hot else 0
															return (
															    [fake_image for _ in range(a		)],
															    [fake_label for _ in range(a		)],
															)
								__UpperCamelCase			:     str         =      self._index_in_epoch
								# Shuffle for the first epoch
								if self._epochs_completed == 0 and start == 0 and shuffle:
															__UpperCamelCase			:     Any         =      numpy.arange(self._num_examples		)
															numpy.random.shuffle(a		)
															__UpperCamelCase			:     int         =      self.images[perma]
															__UpperCamelCase			:     str         =      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
															__UpperCamelCase			:     Optional[int]         =      self._num_examples - start
															__UpperCamelCase			:     Optional[int]         =      self._images[start : self._num_examples]
															__UpperCamelCase			:     int         =      self._labels[start : self._num_examples]
															# Shuffle the data
															if shuffle:
																						__UpperCamelCase			:     Optional[int]         =      numpy.arange(self._num_examples		)
																						numpy.random.shuffle(a		)
																						__UpperCamelCase			:     Optional[Any]         =      self.images[perm]
																						__UpperCamelCase			:     Tuple         =      self.labels[perm]
															# Start next epoch
															__UpperCamelCase			:     Tuple         =      0
															__UpperCamelCase			:     Union[str, Any]         =      batch_size - rest_num_examples
															__UpperCamelCase			:     List[str]         =      self._index_in_epoch
															__UpperCamelCase			:     Dict         =      self._images[start:end]
															__UpperCamelCase			:     str         =      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
															__UpperCamelCase			:     Union[str, Any]         =      self._index_in_epoch
															return self._images[start:end], self._labels[start:end]




@deprecated(_lowerCamelCase		,						"Please write your own downloading logic.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Union[str, Any])      ->					Tuple:






							'''simple docstring'''





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




@deprecated(
    _lowerCamelCase		,						"Please use alternatives such as:" " tensorflow_datasets.load('mnist')")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=False		,						_lowerCamelCase				:    str=False		,						_lowerCamelCase				:    List[str]=dtypes.floataa		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Union[str, Any]=5_000		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    Optional[int]=DEFAULT_SOURCE_URL		,						)      ->					List[Any]:






							'''simple docstring'''





							if fake_data:

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

														__UpperCamelCase			:     Optional[int]         =      fake()
														__UpperCamelCase			:     Tuple         =      fake()
														__UpperCamelCase			:     List[str]         =      fake()
														return _Datasets(train=_lowerCamelCase		,						validation=_lowerCamelCase		,						test=_lowerCamelCase)

							if not source_url:  # empty string check
														__UpperCamelCase			:     str         =      DEFAULT_SOURCE_URL

							__UpperCamelCase			:     Optional[int]         =      "train-images-idx3-ubyte.gz"
							__UpperCamelCase			:     Dict         =      "train-labels-idx1-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-images-idx3-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-labels-idx1-ubyte.gz"

							__UpperCamelCase			:     Optional[int]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_images(_lowerCamelCase)

							__UpperCamelCase			:     Optional[Any]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

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

							__UpperCamelCase			:     str         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + test_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     List[str]         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

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

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

							__UpperCamelCase			:     List[str]         =      {"dtype": dtype, "reshape": reshape, "seed": seed}

							__UpperCamelCase			:     Union[str, Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     str         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)

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