

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'pix2struct_text_model'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{
        'hidden_size': 'hidden_size',
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(      self :       List[Any],lowercase_ :       Union[str, Any]=5_0_2_4_4,lowercase_ :       Optional[int]=7_6_8,lowercase_ :       Tuple=6_4,lowercase_ :       Optional[Any]=2_0_4_8,lowercase_ :       Tuple=1_2,lowercase_ :       List[Any]=1_2,lowercase_ :       Union[str, Any]=3_2,lowercase_ :       Any=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       Tuple=1E-6,lowercase_ :       List[str]=1.0,lowercase_ :       str="gelu_new",lowercase_ :       int=0,lowercase_ :       Union[str, Any]=False,lowercase_ :       Tuple=0,lowercase_ :       Optional[Any]=1,lowercase_ :       List[Any]=False,lowercase_ :       Any=True,**lowercase_ :       Dict,)->    int:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   hidden_size
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   use_cache

        A__          =   eos_token_id
        A__          =   decoder_start_token_id

        # for backwards compatibility
        A__          =   dense_act_fn

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,decoder_start_token_id=lowercase_,tie_word_embeddings=lowercase_,is_decoder=lowercase_,**lowercase_,)


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





        '''simple docstring'''







        cls._set_token_in_kwargs(lowercase_		)

        A__      ,						A__          =   cls.get_config_dict(lowercase_,**lowercase_		)

        # get the text config dict if we are loading from Pix2StructConfig
        if config_dict.get('model_type'		) == "pix2struct":
            A__          =   config_dict['text_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(lowercase_,**lowercase_		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'pix2struct_vision_model'


    def __init__(      self :       int,lowercase_ :       str=7_6_8,lowercase_ :       Optional[Any]=7_6_8,lowercase_ :       Dict=2_0_4_8,lowercase_ :       List[Any]=6_4,lowercase_ :       int=1_2,lowercase_ :       List[str]=1_2,lowercase_ :       List[Any]="gelu_new",lowercase_ :       List[str]=1E-6,lowercase_ :       Optional[Any]=0.0,lowercase_ :       Optional[Any]=0.0,lowercase_ :       Union[str, Any]=1E-10,lowercase_ :       Optional[Any]=1.0,lowercase_ :       List[Any]=4_0_9_6,lowercase_ :       Union[str, Any]=3_2,lowercase_ :       Dict=1_2_8,**lowercase_ :       Dict,)->    List[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)

        A__          =   hidden_size
        A__          =   patch_embed_hidden_size
        A__          =   d_ff
        A__          =   dropout_rate
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   initializer_range
        A__          =   initializer_factor
        A__          =   attention_dropout
        A__          =   layer_norm_eps
        A__          =   dense_act_fn
        A__          =   seq_len
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   d_kv


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





        '''simple docstring'''







        cls._set_token_in_kwargs(lowercase_		)

        A__      ,						A__          =   cls.get_config_dict(lowercase_,**lowercase_		)

        # get the vision config dict if we are loading from Pix2StructConfig
        if config_dict.get('model_type'		) == "pix2struct":
            A__          =   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(lowercase_,**lowercase_		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'pix2struct'
    lowerCamelCase			=							True


    def __init__(      self :       Any,lowercase_ :       Tuple=None,lowercase_ :       List[str]=None,lowercase_ :       Optional[int]=1.0,lowercase_ :       Optional[int]=0.02,lowercase_ :       Tuple=False,lowercase_ :       int=False,lowercase_ :       Any=True,**lowercase_ :       Tuple,)->    Any:





        '''simple docstring'''







        super().__init__(tie_word_embeddings=lowercase_,is_encoder_decoder=lowercase_,**lowercase_		)

        if text_config is None:
            A__          =   {}
            logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'		)

        if vision_config is None:
            A__          =   {}
            logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'		)

        A__          =   PixaStructTextConfig(**lowercase_		)
        A__          =   PixaStructVisionConfig(**lowercase_		)

        A__          =   self.text_config.decoder_start_token_id
        A__          =   self.text_config.pad_token_id
        A__          =   self.text_config.eos_token_id

        A__          =   initializer_factor
        A__          =   initializer_range

        A__          =   self.initializer_range
        A__          =   self.initializer_range

        A__          =   is_vqa


    @classmethod
    def 						snake_case__     (      cls :       List[Any],lowercase_ :       PixaStructTextConfig,lowercase_ :       PixaStructVisionConfig,**lowercase_ :       int		)->    Dict:





        '''simple docstring'''







        return cls(text_config=text_config.to_dict(),vision_config=vision_config.to_dict(),**lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        A__          =   copy.deepcopy(self.__dict__		)
        A__          =   self.text_config.to_dict()
        A__          =   self.vision_config.to_dict()
        A__          =   self.__class__.model_type
        return output




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


lowercase_       		=  "true"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






    A__          =   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
    A__          =   load_dataset('glue'    ,			'mrpc'    ,			split='validation'      )

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   tokenized_datasets.rename_column('label'    ,			'labels'      )

    def collate_fn(SCREAMING_SNAKE_CASE__			:	Dict      ):
        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=128    ,			return_tensors='pt'      )

    return DataLoader(SCREAMING_SNAKE_CASE__    ,			shuffle=SCREAMING_SNAKE_CASE__    ,			collate_fn=SCREAMING_SNAKE_CASE__    ,			batch_size=16      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






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





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






    A__          =   evaluate.load('glue'    ,			'mrpc'      )
    A__      ,						A__          =   get_mrpc_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    # First do baseline
    A__      ,						A__      ,						A__          =   setup['no']
    model.to(SCREAMING_SNAKE_CASE__      )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__      )
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=batch['labels']      )
    A__          =   metric.compute()

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   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 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   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]:
            A__          =   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__    ,			99      )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()

