
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_ :    List[str]     			=						RegressionModel()
    snake_case_ :    Dict     			=						deepcopy(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						RegressionDataset(length=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Dict     			=						DataLoader(__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    model.to(accelerator.device       )
    snake_case_ :    Union[str, Any]     			=						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_ :    List[str]     			=						load_dataset("glue",		"mrpc",		split="validation"       )

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

    with accelerator.main_process_first():
        snake_case_ :    Optional[Any]     			=						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_ :    Optional[int]     			=						Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE,		split_batches=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						get_dataloader(__SCREAMING_SNAKE_CASE,		not dispatch_batches       )
    snake_case_ :    Optional[Any]     			=						AutoModelForSequenceClassification.from_pretrained(
        "hf-internal-testing/mrpc-bert-base-cased",		return_dict=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						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_ :    Dict     			=						[]
    for batch in dataloader:
        snake_case_ :    Dict     			=						batch.values()
        with torch.no_grad():
            snake_case_ :    Union[str, Any]     			=						model(__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Tuple     			=						accelerator.gather_for_metrics((logit, target)       )
            logits_and_targets.append((logit, target)       )
    snake_case_ :    Any     			=						[], []
    for logit, targ in logits_and_targets:
        logits.append(__SCREAMING_SNAKE_CASE       )
        targs.append(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						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_ :    Tuple     			=						get_basic_setup(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						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_ :    Dict     			=						evaluate.load("glue",		"mrpc"       )
    snake_case_ :    List[str]     			=						get_mrpc_setup(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    # First do baseline
    snake_case_ :    Tuple     			=						setup["""no"""]
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()
    for batch in dataloader:
        batch.to(__SCREAMING_SNAKE_CASE       )
        with torch.inference_mode():
            snake_case_ :    Optional[int]     			=						model(**__SCREAMING_SNAKE_CASE       )
        snake_case_ :    int     			=						outputs.logits.argmax(dim=-1       )
        metric.add_batch(predictions=__SCREAMING_SNAKE_CASE,		references=batch["labels"]       )
    snake_case_ :    Optional[int]     			=						metric.compute()

    # Then do distributed
    snake_case_ :    str     			=						setup["""ddp"""]
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            snake_case_ :    Optional[Any]     			=						model(**__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Any     			=						outputs.logits.argmax(dim=-1       )
        snake_case_ :    Dict     			=						batch["""labels"""]
        snake_case_ :    Tuple     			=						accelerator.gather_for_metrics((preds, references)       )
        metric.add_batch(predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						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_ :    Optional[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_ :    Dict     			=						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_ :    List[str]     			=						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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "This is a straightforward English test sentence.",
         "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
         "Now we're going to add some Chinese: 一 二 三 一二三",
         "And some much more rare Chinese: 齉 堃 齉堃",
         "Je vais aussi écrire en français pour tester les accents",
         "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




