



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


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

@dataclass
class    _A	:
       UpperCamelCase__  :   str						 =						field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()   )}   )
       UpperCamelCase__  :   str						 =						field(
           metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}   )
       UpperCamelCase__  :   int						 =						field(
           default=128				,metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }				,)
       UpperCamelCase__  :   bool						 =						field(
           default=__UpperCAmelCase				,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    self.task_name.lower()


class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''train'''
       UpperCamelCase__  :   Dict						 =						'''dev'''
       UpperCamelCase__  :   Optional[Any]						 =						'''test'''


class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   GlueDataTrainingArguments
       UpperCamelCase__  :   str
       UpperCamelCase__  :   List[InputFeatures]



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					GlueDataTrainingArguments					,		__SCREAMING_SNAKE_CASE							:					PreTrainedTokenizerBase					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Union[str, Split] = Split.train					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		):


           '''simple docstring'''

           warnings.warn(
               '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
               '''library. You can have a look at this example script for pointers: '''
               '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''					,		__SCREAMING_SNAKE_CASE					,		)
           __a							=    args
           __a							=    glue_processors[args.task_name]()
           __a							=    glue_output_modes[args.task_name]
           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               try:
                   __a							=    Split[mode]
               except KeyError:
                   raise KeyError('''mode is not a valid split name''')
        # Load data features from cache or dataset file
           __a							=    os.path.join(
               cache_dir if cache_dir is not None else args.data_dir					,		F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'					,		)
           __a							=    self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
               # HACK(label indices are swapped in RoBERTa pretrained model)
               __a	,       __a							=    label_list[2], label_list[1]
           __a							=    label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           __a							=    cached_features_file + '''.lock'''
           with FileLock(__SCREAMING_SNAKE_CASE):
               if os.path.exists(__SCREAMING_SNAKE_CASE) and not args.overwrite_cache:
                   __a							=    time.time()
                   __a							=    torch.load(__SCREAMING_SNAKE_CASE)
                   logger.info(
                       F'Loading features from cached file {cached_features_file} [took %.3f s]'					,		time.time() - start)
               else:
                   logger.info(F'Creating features from dataset file at {args.data_dir}')

                   if mode == Split.dev:
                       __a							=    self.processor.get_dev_examples(args.data_dir)
                   elif mode == Split.test:
                       __a							=    self.processor.get_test_examples(args.data_dir)
                   else:
                       __a							=    self.processor.get_train_examples(args.data_dir)
                   if limit_length is not None:
                       __a							=    examples[:limit_length]
                   __a							=    glue_convert_examples_to_features(
                       __SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		max_length=args.max_seq_length					,		label_list=__SCREAMING_SNAKE_CASE					,		output_mode=self.output_mode					,		)
                   __a							=    time.time()
                   torch.save(self.features					,		__SCREAMING_SNAKE_CASE)
                   # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                   logger.info(
                       F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]')



       def __len__(		self							:					List[str]):


           '''simple docstring'''

           return len(self.features)



       def __getitem__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           return self.features[i]





       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return self.label_list










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__snake_case						:Dict						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		*__SCREAMING_SNAKE_CASE							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           requires_backends(self					,		'''vision''')
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __a							=    {}
           __a							=    {}

           if prompt is not None:
               __a							=    prompt

           if generate_kwargs is not None:
               __a							=    generate_kwargs
           if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                   __a							=    {}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                       ''' please use only one''')
               __a							=    max_new_tokens
           return preprocess_params, forward_kwargs, {}



       def __call__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image.Image", List["Image.Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None):


           '''simple docstring'''

           __a							=    load_image(__SCREAMING_SNAKE_CASE)

           if prompt is not None:
               if not isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. '
                       '''Note also that one single text can be provided for conditional image to text generation.''')

               __a							=    self.model.config.model_type

               if model_type == "git":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(text=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
                   __a							=    [self.tokenizer.cls_token_id] + input_ids
                   __a							=    torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
                   model_inputs.update({'''input_ids''': input_ids})

               elif model_type == "pix2struct":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		header_text=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

               elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   model_inputs.update(__SCREAMING_SNAKE_CASE)

               else:
                   raise ValueError(F'Model type {model_type} does not support conditional text generation')

           else:
               __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
               __a							=    None

           return model_inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']					,		__SCREAMING_SNAKE_CASE)
               and all(x is None for x in model_inputs['''input_ids'''])
           ):
               __a							=    None

           if generate_kwargs is None:
               __a							=    {}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           __a							=    model_inputs.pop(self.model.main_input_name)
           __a							=    self.model.generate(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    []
           for output_ids in model_outputs:
               __a							=    {
                   '''generated_text''': self.tokenizer.decode(
                       __SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE					,		)
               }
               records.append(__SCREAMING_SNAKE_CASE)
           return records





