
'''simple docstring'''
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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=8_2,		__SCREAMING_SNAKE_CASE=1_6       ):
    """simple docstring"""






    set_seed(4_2       )
    snake_case_ :    Optional[int]     			=						RegressionModel()
    snake_case_ :    List[str]     			=						deepcopy(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						RegressionDataset(length=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						DataLoader(__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    model.to(accelerator.device       )
    snake_case_			, snake_case_ :    Dict     			=						accelerator.prepare(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return model, ddp_model, dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"       )
    snake_case_ :    Tuple     			=						load_dataset("glue",		"mrpc",		split="validation"       )

    def tokenize_function(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Optional[int]     			=						tokenizer(examples["sentence1"],		examples["sentence2"],		truncation=__SCREAMING_SNAKE_CASE,		max_length=__SCREAMING_SNAKE_CASE       )
        return outputs

    with accelerator.main_process_first():
        snake_case_ :    Tuple     			=						dataset.map(
            __SCREAMING_SNAKE_CASE,		batched=__SCREAMING_SNAKE_CASE,		remove_columns=["idx", "sentence1", "sentence2"],		)

    snake_case_ :    Dict     			=						tokenized_datasets.rename_column("label",		"labels"       )

    def collate_fn(__SCREAMING_SNAKE_CASE       ):
        if use_longest:
            return tokenizer.pad(__SCREAMING_SNAKE_CASE,		padding="longest",		return_tensors="pt"       )
        return tokenizer.pad(__SCREAMING_SNAKE_CASE,		padding="max_length",		max_length=1_2_8,		return_tensors="pt"       )

    return DataLoader(__SCREAMING_SNAKE_CASE,		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=1_6       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=8_2,		__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=1_6       ):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_ :    List[str]     			=						get_basic_setup(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Union[str, Any]     			=						generate_predictions(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    assert (
        len(__SCREAMING_SNAKE_CASE       ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE       )}'

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = False,		__SCREAMING_SNAKE_CASE = False       ):
    """simple docstring"""






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

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






    snake_case_ :    Union[str, Any]     			=						Accelerator(split_batches=__SCREAMING_SNAKE_CASE,		dispatch_batches=__SCREAMING_SNAKE_CASE       )
    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(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                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]:
            snake_case_ :    int     			=						Accelerator(split_batches=__SCREAMING_SNAKE_CASE,		dispatch_batches=__SCREAMING_SNAKE_CASE       )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'       )
            test_torch_metrics(__SCREAMING_SNAKE_CASE,		9_9       )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print("**Test last batch is not dropped when perfectly divisible**"       )
    snake_case_ :    Any     			=						Accelerator()
    test_torch_metrics(__SCREAMING_SNAKE_CASE,		5_1_2       )
    accelerator.state._reset_state()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()






'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key       )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case_ :    int     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case_ :    Dict     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case_ :    List[str]     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case_ :    str     			=						pt_tensor.transpose(2,		3,		1,		0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # also add unexpected weight so that warning is thrown
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




