




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						List[str]=2							,	__A  :						Dict=3							,	__A  :						List[Any]=6_4							,	__A  :						Dict=None ):
						snake_case__				:   List[Any]     =						np.random.default_rng(__a )
						snake_case__				:   Dict     =						length
						snake_case__				:   str     =						rng.normal(size=(length,) ).astype(np.floataa )
						snake_case__				:   int     =						a * self.x + b + rng.normal(scale=0.1							,	size=(length,) ).astype(np.floataa )



					def __len__(      self  :						List[Any] ):
						return self.length






					def __getitem__(      self  :						Optional[int]							,	__A  :						int ):
						return {"x": self.x[i], "y": self.y[i]}



class 							SCREAMING_SNAKE_CASE__		(       torch.nn.Module		):





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						int=0							,	__A  :						Union[str, Any]=0							,	__A  :						List[Any]=False ):
						super().__init__()
						snake_case__				:   Optional[Any]     =						torch.nn.Parameter(torch.tensor([2, 3] ).float() )
						snake_case__				:   List[Any]     =						torch.nn.Parameter(torch.tensor([2, 3] ).float() )
						snake_case__				:   str     =						True






					def  _lowercase  (      self  :						List[Any]							,	__A  :						Optional[Any]=None ):






						if self.first_batch:
							print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
							snake_case__				:   Optional[Any]     =						False
						return x * self.a[0] + self.b[0]



class 							SCREAMING_SNAKE_CASE__		(       torch.nn.Module		):





					"""simple docstring"""






					def __init__(      self  :						Any							,	__A  :						List[Any]=0							,	__A  :						List[str]=0							,	__A  :						Union[str, Any]=False ):
						super().__init__()
						snake_case__				:   int     =						torch.nn.Parameter(torch.tensor(__a ).float() )
						snake_case__				:   Tuple     =						torch.nn.Parameter(torch.tensor(__a ).float() )
						snake_case__				:   List[str]     =						True






					def  _lowercase  (      self  :						Any							,	__A  :						List[str]=None ):






						if self.first_batch:
							print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
							snake_case__				:   Dict     =						False
						return x * self.a + self.b







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							int = 16     ):
	from datasets import load_dataset
	from transformers import AutoTokenizer

	snake_case__				:   List[str]     =						AutoTokenizer.from_pretrained("bert-base-cased"     )
	snake_case__				:   Optional[Any]     =						{'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
	snake_case__				:   Tuple     =						load_dataset("csv"  ,						data_files=snake_case_     )
	snake_case__				:   Optional[int]     =						datasets['train'].unique("label"     )

	snake_case__				:   Optional[Any]     =						{v: i for i, v in enumerate(snake_case_     )}

	def tokenize_function(snake_case_      :							List[Any]     ):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__				:   Tuple     =						tokenizer(
		    examples["sentence1"]  ,						examples["sentence2"]  ,						truncation=snake_case_  ,						max_length=snake_case_  ,						padding="max_length"     )
		if "label" in examples:
			snake_case__				:   Tuple     =						[label_to_id[l] for l in examples['label']]
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	snake_case__				:   List[str]     =						datasets.map(
	    snake_case_  ,						batched=snake_case_  ,						remove_columns=["sentence1", "sentence2", "label"]  ,						)

	def collate_fn(snake_case_      :							Dict     ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(snake_case_  ,						padding="max_length"  ,						max_length=128  ,						return_tensors="pt"     )
		return tokenizer.pad(snake_case_  ,						padding="longest"  ,						return_tensors="pt"     )

	# Instantiate dataloaders.
	snake_case__				:   Tuple     =						DataLoader(tokenized_datasets["train"]  ,						shuffle=snake_case_  ,						collate_fn=snake_case_  ,						batch_size=2     )
	snake_case__				:   Union[str, Any]     =						DataLoader(tokenized_datasets["validation"]  ,						shuffle=snake_case_  ,						collate_fn=snake_case_  ,						batch_size=1     )

	return train_dataloader, eval_dataloader











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      :    Union[str, Any]   		=   logging.get_logger(__name__)

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



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_vision_model"



					def __init__(      self  :						List[Any]							,	__A  :						Dict=1_4_0_8							,	__A  :						Tuple=6_1_4_4							,	__A  :						str=3_9							,	__A  :						int=1_6							,	__A  :						str=2_2_4							,	__A  :						Any=1_4							,	__A  :						Dict="gelu"							,	__A  :						List[Any]=1e-6							,	__A  :						Any=0.0							,	__A  :						List[Any]=1e-1_0							,	__A  :						Union[str, Any]=True							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

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






					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[Any] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   str     =						cls.get_config_dict(__A							,	**__A )

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   Union[str, Any]     =						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(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_qformer"



					def __init__(      self  :						Any							,	__A  :						Union[str, Any]=3_0_5_2_2							,	__A  :						Union[str, Any]=7_6_8							,	__A  :						Optional[int]=1_2							,	__A  :						Dict=1_2							,	__A  :						Dict=3_0_7_2							,	__A  :						List[str]="gelu"							,	__A  :						Union[str, Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=5_1_2							,	__A  :						Optional[int]=0.0_2							,	__A  :						List[str]=1e-1_2							,	__A  :						Any=0							,	__A  :						Optional[Any]="absolute"							,	__A  :						str=2							,	__A  :						Any=1_4_0_8							,	**__A  :						List[str]							,	):
						super().__init__(pad_token_id=__A							,	**__A )

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






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Tuple     =						cls.get_config_dict(__A							,	**__A )

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   List[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(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip"
					a_			=	True



					def __init__(      self  :						List[str]							,	__A  :						Optional[Any]=None							,	__A  :						Tuple=None							,	__A  :						Optional[int]=None							,	__A  :						Optional[Any]=3_2							,	**__A  :						Optional[int] ):
						super().__init__(**__A )

						if vision_config is None:
							snake_case__				:   Any     =						{}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )

						if qformer_config is None:
							snake_case__				:   Optional[Any]     =						{}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )

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

						snake_case__				:   List[Any]     =						InstructBlipVisionConfig(**__A )
						snake_case__				:   Union[str, Any]     =						InstructBlipQFormerConfig(**__A )
						snake_case__				:   Dict     =						text_config["model_type"] if "model_type" in text_config else "opt"
						snake_case__				:   List[Any]     =						CONFIG_MAPPING[text_model_type](**__A )

						snake_case__				:   Union[str, Any]     =						self.text_config.tie_word_embeddings
						snake_case__				:   Tuple     =						self.text_config.is_encoder_decoder

						snake_case__				:   str     =						num_query_tokens
						snake_case__				:   Dict     =						self.vision_config.hidden_size
						snake_case__				:   List[Any]     =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						snake_case__				:   int     =						1.0
						snake_case__				:   Optional[int]     =						0.0_2



					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						InstructBlipVisionConfig							,	__A  :						InstructBlipQFormerConfig							,	__A  :						PretrainedConfig							,	**__A  :						int							,	):
						return cls(
						    vision_config=vision_config.to_dict()							,	qformer_config=qformer_config.to_dict()							,	text_config=text_config.to_dict()							,	**__A							,	)






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



