

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_      =     'true'

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=16):
	set_seed(42)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							RegressionModel()
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							deepcopy(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							RegressionDataset(length=_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							DataLoader(_a				,      batch_size=_a)
	model.to(accelerator.device)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							accelerator.prepare(_a				,      _a)
	return model, ddp_model, dataloader

def 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_dataset("glue"				,      "mrpc"				,      split="validation")

	def tokenize_function(_a):
		SCREAMING_SNAKE_CASE :   str					  =							tokenizer(examples["sentence1"]				,      examples["sentence2"]				,      truncation=_a				,      max_length=_a)
		return outputs

	with accelerator.main_process_first():
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dataset.map(
		    _a				,      batched=_a				,      remove_columns=["idx", "sentence1", "sentence2"]				,      )

	SCREAMING_SNAKE_CASE :   Any					  =							tokenized_datasets.rename_column("label"				,      "labels")

	def collate_fn(_a):
		if use_longest:
			return tokenizer.pad(_a				,      padding="longest"				,      return_tensors="pt")
		return tokenizer.pad(_a				,      padding="max_length"				,      max_length=128				,      return_tensors="pt")

	return DataLoader(_a				,      shuffle=_a				,      collate_fn=_a				,      batch_size=16)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							Accelerator(dispatch_batches=_a				,      split_batches=_a)
	SCREAMING_SNAKE_CASE :   str					  =							get_dataloader(_a				,      not dispatch_batches)
	SCREAMING_SNAKE_CASE :   Any					  =							AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"				,      return_dict=_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							accelerator.prepare(_a				,      _a)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	for batch in dataloader:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							batch.values()
		with torch.no_grad():
			SCREAMING_SNAKE_CASE :   str					  =							model(_a)
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							accelerator.gather_for_metrics((logit, target))
			logits_and_targets.append((logit, target))
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							[], []
	for logit, targ in logits_and_targets:
		logits.append(_a)
		targs.append(_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							torch.cat(_a), torch.cat(_a)
	return logits, targs

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=False				,      _a=False				,      _a=16):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_basic_setup(_a				,      _a				,      _a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							generate_predictions(_a				,      _a				,      _a)
	assert (
	    len(_a) == num_samples
	), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a)}"

def 			lowerCamelCase__			(       _a = False				,      _a = False):
	SCREAMING_SNAKE_CASE :   List[str]					  =							evaluate.load("glue"				,      "mrpc")
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_mrpc_setup(_a				,      _a)
	# First do baseline
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							setup["no"]
	model.to(_a)
	model.eval()
	for batch in dataloader:
		batch.to(_a)
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							outputs.logits.argmax(dim=-1)
		metric.add_batch(predictions=_a				,      references=batch["labels"])
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	# Then do distributed
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.logits.argmax(dim=-1)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							batch["labels"]
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							accelerator.gather_for_metrics((preds, references))
		metric.add_batch(predictions=_a				,      references=_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]				,      distributed[key]), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print("**Testing gather_for_metrics**")
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
				test_mrpc(_a				,      _a)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test torch metrics**")
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
			if accelerator.is_local_main_process:
				print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99")
			test_torch_metrics(_a				,      99)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**")
	SCREAMING_SNAKE_CASE :   str					  =							Accelerator()
	test_torch_metrics(_a				,      512)
	accelerator.state._reset_state()

def 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)