


"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase   :Dict						     =		logging.get_logger(__name__)

__lowerCamelCase   :str						     =		{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""
     snake_case__						:       Optional[int]				    ='''instructblip_vision_model'''

     def __init__(						self:    str  ,		__a:    Tuple=1_408  ,		__a:    Optional[Any]=6_144  ,		__a:    List[Any]=39  ,		__a:    int=16  ,		__a:    List[Any]=224  ,		__a:    List[str]=14  ,		__a:    List[Any]="gelu"  ,		__a:    List[str]=1e-6  ,		__a:    Tuple=0.0  ,		__a:    Any=1e-1_0  ,		__a:    int=True  ,		**__a:    Any  ,		)->     str:
           super().__init__(**_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       List[str]   =						hidden_size
           lowerCamelCase       :       Dict   =						intermediate_size
           lowerCamelCase       :       Optional[int]   =						num_hidden_layers
           lowerCamelCase       :       List[str]   =						num_attention_heads
           lowerCamelCase       :       int   =						patch_size
           lowerCamelCase       :       Dict   =						image_size
           lowerCamelCase       :       Dict   =						initializer_range
           lowerCamelCase       :       Any   =						attention_dropout
           lowerCamelCase       :       Optional[Any]   =						layer_norm_eps
           lowerCamelCase       :       Tuple   =						hidden_act
           lowerCamelCase       :       Optional[Any]   =						qkv_bias







     @classmethod
     def 		a__    (						cls:    int  ,		__a:    Union[str, os.PathLike]  ,		**__a:    Union[str, Any]						)->     "PretrainedConfig":
           cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       Optional[Any]   =						cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)

           # get the vision config dict if we are loading from InstructBlipConfig
           if config_dict.get("""model_type"""						) == "instructblip":
                 lowerCamelCase       :       Tuple   =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls  ,		"""model_type"""						) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                     f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""
     snake_case__						:       Any				    ='''instructblip_qformer'''

     def __init__(						self:    Optional[Any]  ,		__a:    Optional[Any]=30_522  ,		__a:    str=768  ,		__a:    str=12  ,		__a:    Any=12  ,		__a:    List[str]=3_072  ,		__a:    List[Any]="gelu"  ,		__a:    Optional[int]=0.1  ,		__a:    List[Any]=0.1  ,		__a:    Optional[Any]=512  ,		__a:    Dict=0.02  ,		__a:    Tuple=1e-1_2  ,		__a:    Dict=0  ,		__a:    int="absolute"  ,		__a:    List[Any]=2  ,		__a:    Optional[Any]=1_408  ,		**__a:    int  ,		)->     Union[str, Any]:
           super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       Optional[Any]   =						vocab_size
           lowerCamelCase       :       int   =						hidden_size
           lowerCamelCase       :       List[Any]   =						num_hidden_layers
           lowerCamelCase       :       int   =						num_attention_heads
           lowerCamelCase       :       Any   =						hidden_act
           lowerCamelCase       :       Tuple   =						intermediate_size
           lowerCamelCase       :       int   =						hidden_dropout_prob
           lowerCamelCase       :       Optional[int]   =						attention_probs_dropout_prob
           lowerCamelCase       :       int   =						max_position_embeddings
           lowerCamelCase       :       Optional[Any]   =						initializer_range
           lowerCamelCase       :       Optional[int]   =						layer_norm_eps
           lowerCamelCase       :       str   =						position_embedding_type
           lowerCamelCase       :       Tuple   =						cross_attention_frequency
           lowerCamelCase       :       Any   =						encoder_hidden_size







     @classmethod
     def 		a__    (						cls:    Dict  ,		__a:    Union[str, os.PathLike]  ,		**__a:    Union[str, Any]						)->     "PretrainedConfig":
           cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       str   =						cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)

           # get the qformer config dict if we are loading from InstructBlipConfig
           if config_dict.get("""model_type"""						) == "instructblip":
                 lowerCamelCase       :       str   =						config_dict["qformer_config"]

           if "model_type" in config_dict and hasattr(cls  ,		"""model_type"""						) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                     f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""
     snake_case__						:       str				    ='''instructblip'''
     snake_case__						:       List[Any]				    =True

     def __init__(						self:    int  ,		__a:    int=None  ,		__a:    List[str]=None  ,		__a:    Tuple=None  ,		__a:    Tuple=32  ,		**__a:    Tuple						)->     Optional[Any]:
           super().__init__(**_SCREAMING_SNAKE_CASE						)

           if vision_config is None:
                 lowerCamelCase       :       Union[str, Any]   =						{}
                 logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""						)

           if qformer_config is None:
                 lowerCamelCase       :       int   =						{}
                 logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""						)

           if text_config is None:
                 lowerCamelCase       :       Optional[Any]   =						{}
                 logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""						)

           lowerCamelCase       :       int   =						InstructBlipVisionConfig(**_SCREAMING_SNAKE_CASE						)
           lowerCamelCase       :       List[str]   =						InstructBlipQFormerConfig(**_SCREAMING_SNAKE_CASE						)
           lowerCamelCase       :       Union[str, Any]   =						text_config["model_type"] if "model_type" in text_config else "opt"
           lowerCamelCase       :       Any   =						CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       int   =						self.text_config.tie_word_embeddings
           lowerCamelCase       :       Any   =						self.text_config.is_encoder_decoder

           lowerCamelCase       :       List[Any]   =						num_query_tokens
           lowerCamelCase       :       List[str]   =						self.vision_config.hidden_size
           lowerCamelCase       :       Any   =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           lowerCamelCase       :       Union[str, Any]   =						1.0
           lowerCamelCase       :       List[Any]   =						0.02

     @classmethod
     def 		a__    (						cls:    int  ,		__a:    InstructBlipVisionConfig  ,		__a:    InstructBlipQFormerConfig  ,		__a:    PretrainedConfig  ,		**__a:    Union[str, Any]  ,		)->     str:
           return cls(
               vision_config=vision_config.to_dict()  ,		qformer_config=qformer_config.to_dict()  ,		text_config=text_config.to_dict()  ,		**_SCREAMING_SNAKE_CASE  ,		)







     def 		a__    (						self:    Tuple						)->     Optional[int]:
           lowerCamelCase       :       str   =						copy.deepcopy(self.__dict__						)
           lowerCamelCase       :       str   =						self.vision_config.to_dict()
           lowerCamelCase       :       str   =						self.qformer_config.to_dict()
           lowerCamelCase       :       str   =						self.text_config.to_dict()
           lowerCamelCase       :       Tuple   =						self.__class__.model_type
           return output









"""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

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

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


def      _lowercase							(    __snake_case      )		->  List[Any]:
			__lowerCAmelCase :    int				=			numpy.dtype(numpy.uintaa      ).newbyteorder(">"      )
			return numpy.frombuffer(bytestream.read(4      )   ,dtype=__snake_case      )[0]


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case      )		->  Union[str, Any]:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						if magic != 2_051:
									raise ValueError(
									    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			bytestream.read(rows * cols * num_images      )
						__lowerCAmelCase :    Optional[Any]				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						__lowerCAmelCase :    str				=			data.reshape(__snake_case   ,__snake_case   ,__snake_case   ,1      )
						return data


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


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=10      )		->  str:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						if magic != 2_049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			bytestream.read(__snake_case      )
						__lowerCAmelCase :    Dict				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						if one_hot:
									return _dense_to_one_hot(__snake_case   ,__snake_case      )
						return labels



class      A__  :




	'''simple docstring'''





	@deprecated(
	    _SCREAMING_SNAKE_CASE			,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."			,     )
	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						str=dtypes.floataa			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			random_seed.get_seed(_SCREAMING_SNAKE_CASE)
				# 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 :    Optional[Any]				=			dtypes.as_dtype(_SCREAMING_SNAKE_CASE).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 :    Tuple				=			1_0000
							__lowerCAmelCase :    Optional[Any]				=			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 :    Dict				=			images.astype(numpy.floataa)
										__lowerCAmelCase :    int				=			numpy.multiply(_SCREAMING_SNAKE_CASE			,     1.0 / 255.0)
				__lowerCAmelCase :    Optional[Any]				=			images
				__lowerCAmelCase :    int				=			labels
				__lowerCAmelCase :    Optional[Any]				=			0
				__lowerCAmelCase :    Optional[int]				=			0

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				return self._images

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			int:

				"""simple docstring"""

				return self._labels

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			str:

				"""simple docstring"""

				return self._num_examples

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

				return self._epochs_completed







	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						List[str]=True)							->			int:

				"""simple docstring"""

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






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





@deprecated(
    __snake_case   ,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=False   ,__snake_case=dtypes.floataa   ,__snake_case=True   ,__snake_case=5_000   ,__snake_case=None   ,__snake_case=DEFAULT_SOURCE_URL   ,)		->  Tuple:
			if fake_data:

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

						__lowerCAmelCase :    Union[str, Any]				=			fake()
						__lowerCAmelCase :    Optional[Any]				=			fake()
						__lowerCAmelCase :    List[Any]				=			fake()
						return _Datasets(train=__snake_case   ,validation=__snake_case   ,test=__snake_case      )

			if not source_url:  # empty string check
						__lowerCAmelCase :    Optional[Any]				=			DEFAULT_SOURCE_URL

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

			__lowerCAmelCase :    Any				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			_extract_images(__snake_case      )

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

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

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

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

			__lowerCAmelCase :    Any				=			train_images[:validation_size]
			__lowerCAmelCase :    Any				=			train_labels[:validation_size]
			__lowerCAmelCase :    List[str]				=			train_images[validation_size:]
			__lowerCAmelCase :    Optional[Any]				=			train_labels[validation_size:]

			__lowerCAmelCase :    Dict				=			{"dtype": dtype, "reshape": reshape, "seed": seed}

			__lowerCAmelCase :    str				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Dict				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )

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