

"""simple docstring"""


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

__A						       =      logging.getLogger(__name__)





@dataclass
class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  field(
				    default=1_28,							metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}  )
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """Whether to pad all samples to `max_seq_length`. """
				            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
				            """value if set."""
				        )
				    },							)





@dataclass
class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				str       				=  field(
				    default=__snake_case,							metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
				SCREAMING_SNAKE_CASE_       :				str       				=  field(
				    default=__snake_case,							metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}  )
				SCREAMING_SNAKE_CASE_       :				Optional[str]       				=  field(
				    default=__snake_case,							metadata={"""help""": """Train language if it is different from the evaluation language."""}  )
				SCREAMING_SNAKE_CASE_       :				Optional[str]       				=  field(
				    default=__snake_case,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
				SCREAMING_SNAKE_CASE_       :				Optional[str]       				=  field(
				    default=__snake_case,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
				SCREAMING_SNAKE_CASE_       :				Optional[str]       				=  field(
				    default=__snake_case,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},							)
				SCREAMING_SNAKE_CASE_       :				Optional[bool]       				=  field(
				    default=__snake_case,							metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""},							)
				SCREAMING_SNAKE_CASE_       :				str       				=  field(
				    default="""main""",							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""},							)






def    a__   (   )     ->     Optional[int]:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		__lowerCAmelCase:	Tuple   				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
		__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_xnli" ,			__SCREAMING_SNAKE_CASE			)

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,			datefmt="%m/%d/%Y %H:%M:%S" ,			handlers=[logging.StreamHandler(sys.stdout			)] ,			)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		__lowerCAmelCase:	Tuple   				=      training_args.get_process_log_level()
		logger.setLevel(__SCREAMING_SNAKE_CASE			)
		datasets.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE			)
		transformers.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE			)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
		    + F"distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}"			)
		logger.info(F"Training/evaluation parameters {training_args}"			)

		# Detecting last checkpoint.
		__lowerCAmelCase:	List[Any]   				=      None
		if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
				__lowerCAmelCase:	Optional[Any]   				=      get_last_checkpoint(training_args.output_dir			)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
						raise ValueError(
						    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
						    "Use --overwrite_output_dir to overcome."			)
				elif last_checkpoint is not None:
						logger.info(
						    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Set seed before initializing model.
		set_seed(training_args.seed			)

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						__lowerCAmelCase:	Union[str, Any]   				=      load_dataset(
						    "xnli" ,			model_args.language ,			split="train" ,			cache_dir=model_args.cache_dir ,			use_auth_token=True if model_args.use_auth_token else None ,			)
				else:
						__lowerCAmelCase:	Dict   				=      load_dataset(
						    "xnli" ,			model_args.train_language ,			split="train" ,			cache_dir=model_args.cache_dir ,			use_auth_token=True if model_args.use_auth_token else None ,			)
				__lowerCAmelCase:	str   				=      train_dataset.features["label"].names

		if training_args.do_eval:
				__lowerCAmelCase:	Union[str, Any]   				=      load_dataset(
				    "xnli" ,			model_args.language ,			split="validation" ,			cache_dir=model_args.cache_dir ,			use_auth_token=True if model_args.use_auth_token else None ,			)
				__lowerCAmelCase:	List[Any]   				=      eval_dataset.features["label"].names

		if training_args.do_predict:
				__lowerCAmelCase:	Union[str, Any]   				=      load_dataset(
				    "xnli" ,			model_args.language ,			split="test" ,			cache_dir=model_args.cache_dir ,			use_auth_token=True if model_args.use_auth_token else None ,			)
				__lowerCAmelCase:	List[Any]   				=      predict_dataset.features["label"].names

		# Labels
		__lowerCAmelCase:	List[Any]   				=      len(__SCREAMING_SNAKE_CASE			)

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		__lowerCAmelCase:	str   				=      AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,			num_labels=__SCREAMING_SNAKE_CASE ,			idalabel={str(__SCREAMING_SNAKE_CASE			): label for i, label in enumerate(__SCREAMING_SNAKE_CASE			)} ,			labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE			)} ,			finetuning_task="xnli" ,			cache_dir=model_args.cache_dir ,			revision=model_args.model_revision ,			use_auth_token=True if model_args.use_auth_token else None ,			)
		__lowerCAmelCase:	Any   				=      AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,			do_lower_case=model_args.do_lower_case ,			cache_dir=model_args.cache_dir ,			use_fast=model_args.use_fast_tokenizer ,			revision=model_args.model_revision ,			use_auth_token=True if model_args.use_auth_token else None ,			)
		__lowerCAmelCase:	Union[str, Any]   				=      AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path ,			from_tf=bool(".ckpt" in model_args.model_name_or_path			) ,			config=__SCREAMING_SNAKE_CASE ,			cache_dir=model_args.cache_dir ,			revision=model_args.model_revision ,			use_auth_token=True if model_args.use_auth_token else None ,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,			)

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				__lowerCAmelCase:	List[str]   				=      "max_length"
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				__lowerCAmelCase:	Dict   				=      False

		def preprocess_function(__SCREAMING_SNAKE_CASE			):
				# Tokenize the texts
				return tokenizer(
				    examples["premise"] ,			examples["hypothesis"] ,			padding=__SCREAMING_SNAKE_CASE ,			max_length=data_args.max_seq_length ,			truncation=__SCREAMING_SNAKE_CASE ,			)

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						__lowerCAmelCase:	str   				=      min(len(__SCREAMING_SNAKE_CASE			) ,			data_args.max_train_samples			)
						__lowerCAmelCase:	Optional[Any]   				=      train_dataset.select(range(__SCREAMING_SNAKE_CASE			)			)
				with training_args.main_process_first(desc="train dataset map pre-processing"			):
						__lowerCAmelCase:	str   				=      train_dataset.map(
						    __SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE ,			load_from_cache_file=not data_args.overwrite_cache ,			desc="Running tokenizer on train dataset" ,			)
				# Log a few random samples from the training set:
				for index in random.sample(range(len(__SCREAMING_SNAKE_CASE			)			) ,			3			):
						logger.info(F"Sample {index} of the training set: {train_dataset[index]}."			)

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						__lowerCAmelCase:	Union[str, Any]   				=      min(len(__SCREAMING_SNAKE_CASE			) ,			data_args.max_eval_samples			)
						__lowerCAmelCase:	Dict   				=      eval_dataset.select(range(__SCREAMING_SNAKE_CASE			)			)
				with training_args.main_process_first(desc="validation dataset map pre-processing"			):
						__lowerCAmelCase:	List[str]   				=      eval_dataset.map(
						    __SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE ,			load_from_cache_file=not data_args.overwrite_cache ,			desc="Running tokenizer on validation dataset" ,			)

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						__lowerCAmelCase:	List[str]   				=      min(len(__SCREAMING_SNAKE_CASE			) ,			data_args.max_predict_samples			)
						__lowerCAmelCase:	Any   				=      predict_dataset.select(range(__SCREAMING_SNAKE_CASE			)			)
				with training_args.main_process_first(desc="prediction dataset map pre-processing"			):
						__lowerCAmelCase:	int   				=      predict_dataset.map(
						    __SCREAMING_SNAKE_CASE ,			batched=__SCREAMING_SNAKE_CASE ,			load_from_cache_file=not data_args.overwrite_cache ,			desc="Running tokenizer on prediction dataset" ,			)

    # Get the metric function
		__lowerCAmelCase:	str   				=      evaluate.load("xnli"			)

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Any   				=      p.predictions[0] if isinstance(p.predictions ,			__SCREAMING_SNAKE_CASE			) else p.predictions
				__lowerCAmelCase:	Optional[Any]   				=      np.argmax(__SCREAMING_SNAKE_CASE ,			axis=1			)
				return metric.compute(predictions=__SCREAMING_SNAKE_CASE ,			references=p.label_ids			)

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				__lowerCAmelCase:	Union[str, Any]   				=      default_data_collator
		elif training_args.fpaa:
				__lowerCAmelCase:	Optional[Any]   				=      DataCollatorWithPadding(__SCREAMING_SNAKE_CASE ,			pad_to_multiple_of=8			)
		else:
				__lowerCAmelCase:	List[str]   				=      None

		# Initialize our Trainer
		__lowerCAmelCase:	Optional[Any]   				=      Trainer(
		    model=__SCREAMING_SNAKE_CASE ,			args=__SCREAMING_SNAKE_CASE ,			train_dataset=train_dataset if training_args.do_train else None ,			eval_dataset=eval_dataset if training_args.do_eval else None ,			compute_metrics=__SCREAMING_SNAKE_CASE ,			tokenizer=__SCREAMING_SNAKE_CASE ,			data_collator=__SCREAMING_SNAKE_CASE ,			)

		# Training
		if training_args.do_train:
				__lowerCAmelCase:	Optional[int]   				=      None
				if training_args.resume_from_checkpoint is not None:
						__lowerCAmelCase:	List[Any]   				=      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						__lowerCAmelCase:	Any   				=      last_checkpoint
				__lowerCAmelCase:	List[str]   				=      trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	List[str]   				=      train_result.metrics
				__lowerCAmelCase:	Optional[Any]   				=      (
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(__SCREAMING_SNAKE_CASE			)
				)
				__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			len(__SCREAMING_SNAKE_CASE			)			)

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics("train" ,			__SCREAMING_SNAKE_CASE			)
				trainer.save_metrics("train" ,			__SCREAMING_SNAKE_CASE			)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info("*** Evaluate ***"			)
				__lowerCAmelCase:	Optional[Any]   				=      trainer.evaluate(eval_dataset=__SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase:	Optional[int]   				=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	int   				=      min(__SCREAMING_SNAKE_CASE ,			len(__SCREAMING_SNAKE_CASE			)			)

				trainer.log_metrics("eval" ,			__SCREAMING_SNAKE_CASE			)
				trainer.save_metrics("eval" ,			__SCREAMING_SNAKE_CASE			)

		# Prediction
		if training_args.do_predict:
				logger.info("*** Predict ***"			)
				__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	Any   				=      trainer.predict(__SCREAMING_SNAKE_CASE ,			metric_key_prefix="predict"			)

				__lowerCAmelCase:	Any   				=      (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__SCREAMING_SNAKE_CASE			)
				)
				__lowerCAmelCase:	Dict   				=      min(__SCREAMING_SNAKE_CASE ,			len(__SCREAMING_SNAKE_CASE			)			)

				trainer.log_metrics("predict" ,			__SCREAMING_SNAKE_CASE			)
				trainer.save_metrics("predict" ,			__SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase:	Dict   				=      np.argmax(__SCREAMING_SNAKE_CASE ,			axis=1			)
				__lowerCAmelCase:	Any   				=      os.path.join(training_args.output_dir ,			"predictions.txt"			)
				if trainer.is_world_process_zero():
						with open(__SCREAMING_SNAKE_CASE ,			"w"			) as writer:
								writer.write("index\tprediction\n"			)
								for index, item in enumerate(__SCREAMING_SNAKE_CASE			):
										__lowerCAmelCase:	List[Any]   				=      label_list[item]
										writer.write(F"{index}\t{item}\n"			)


if __name__ == "__main__":
			main()





"""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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """blip_2_vision_model"""

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[int]=1_4_0_8					,  UpperCamelCase__							:    Tuple=6_1_4_4					,  UpperCamelCase__							:    Tuple=3_9					,  UpperCamelCase__							:    str=1_6					,  UpperCamelCase__							:    Optional[Any]=2_2_4					,  UpperCamelCase__							:    List[str]=1_4					,  UpperCamelCase__							:    Optional[int]="gelu"					,  UpperCamelCase__							:    Optional[int]=0.00001					,  UpperCamelCase__							:    Tuple=0.0					,  UpperCamelCase__							:    Dict=1e-10					,  UpperCamelCase__							:    List[str]=True					,  **UpperCamelCase__							:    List[str]					,  )->       int:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

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




				@classmethod
				def 							lowercase_					(     cls							:    List[str]					,  UpperCamelCase__							:    Union[str, os.PathLike]					,  **UpperCamelCase__							:    List[str])->       "PretrainedConfig":



						'''simple docstring'''


						cls._set_token_in_kwargs(UpperCamelCase__)

						__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      cls.get_config_dict(UpperCamelCase__					,  **UpperCamelCase__)

						# get the vision config dict if we are loading from Blip2Config
						if config_dict.get("model_type") == "blip-2":
								__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(UpperCamelCase__					,  **UpperCamelCase__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  """blip_2_qformer"""

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    Dict=3_0_5_2_2					,  UpperCamelCase__							:    Union[str, Any]=7_6_8					,  UpperCamelCase__							:    Union[str, Any]=1_2					,  UpperCamelCase__							:    Union[str, Any]=1_2					,  UpperCamelCase__							:    Optional[int]=3_0_7_2					,  UpperCamelCase__							:    Optional[Any]="gelu"					,  UpperCamelCase__							:    Tuple=0.1					,  UpperCamelCase__							:    Optional[Any]=0.1					,  UpperCamelCase__							:    List[str]=5_1_2					,  UpperCamelCase__							:    List[Any]=0.02					,  UpperCamelCase__							:    Dict=1e-12					,  UpperCamelCase__							:    List[str]=0					,  UpperCamelCase__							:    Optional[int]="absolute"					,  UpperCamelCase__							:    List[Any]=2					,  UpperCamelCase__							:    Optional[Any]=1_4_0_8					,  **UpperCamelCase__							:    List[str]					,  )->       str:



						'''simple docstring'''


						super().__init__(pad_token_id=UpperCamelCase__					,  **UpperCamelCase__)

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




				@classmethod
				def 							lowercase_					(     cls							:    int					,  UpperCamelCase__							:    Union[str, os.PathLike]					,  **UpperCamelCase__							:    Tuple)->       "PretrainedConfig":



						'''simple docstring'''


						cls._set_token_in_kwargs(UpperCamelCase__)

						__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      cls.get_config_dict(UpperCamelCase__					,  **UpperCamelCase__)

						# get the qformer config dict if we are loading from Blip2Config
						if config_dict.get("model_type") == "blip-2":
								__lowerCAmelCase:	Any   				=      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(UpperCamelCase__					,  **UpperCamelCase__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  """blip-2"""
				SCREAMING_SNAKE_CASE_       :				str       				=  True

				def __init__(     self							:    List[Any]					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    Dict=None					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    Union[str, Any]=3_2					,  **UpperCamelCase__							:    Dict)->       List[Any]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						if vision_config is None:
								__lowerCAmelCase:	Dict   				=      {}
								logger.info("vision_config is None. initializing the Blip2VisionConfig with default values.")

						if qformer_config is None:
								__lowerCAmelCase:	Union[str, Any]   				=      {}
								logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values.")

						if text_config is None:
								__lowerCAmelCase:	Dict   				=      {}
								logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")

						__lowerCAmelCase:	List[str]   				=      BlipaVisionConfig(**UpperCamelCase__)
						__lowerCAmelCase:	str   				=      BlipaQFormerConfig(**UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      text_config["model_type"] if "model_type" in text_config else "opt"
						__lowerCAmelCase:	Optional[int]   				=      CONFIG_MAPPING[text_model_type](**UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      self.text_config.tie_word_embeddings
						__lowerCAmelCase:	List[Any]   				=      self.text_config.is_encoder_decoder

						__lowerCAmelCase:	int   				=      num_query_tokens
						__lowerCAmelCase:	Union[str, Any]   				=      self.vision_config.hidden_size
						__lowerCAmelCase:	Any   				=      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						__lowerCAmelCase:	Dict   				=      1.0
						__lowerCAmelCase:	Dict   				=      0.02

				@classmethod
				def 							lowercase_					(     cls							:    str					,  UpperCamelCase__							:    BlipaVisionConfig					,  UpperCamelCase__							:    BlipaQFormerConfig					,  UpperCamelCase__							:    PretrainedConfig					,  **UpperCamelCase__							:    Union[str, Any]					,  )->       Tuple:



						'''simple docstring'''


						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **UpperCamelCase__					,  )




				def 							lowercase_					(     self							:    Any)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      copy.deepcopy(self.__dict__)
						__lowerCAmelCase:	Optional[int]   				=      self.vision_config.to_dict()
						__lowerCAmelCase:	List[Any]   				=      self.qformer_config.to_dict()
						__lowerCAmelCase:	str   				=      self.text_config.to_dict()
						__lowerCAmelCase:	Tuple   				=      self.__class__.model_type
						return output


