



'''simple docstring'''

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					@slow
					@require_torch
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
						_lowercase					:       Tuple       =		BertTokenizer.from_pretrained('bert-base-uncased'					)

						_lowercase					:       Optional[Any]       =		bertabert.config.encoder.vocab_size
						_lowercase					:       Optional[Any]       =		tokenizer.sep_token_id
						_lowercase					:       Any       =		tokenizer.cls_token_id
						_lowercase					:       Tuple       =		128

						_lowercase					:       Any       =		datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
						_lowercase					:       Tuple       =		datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

						_lowercase					:       Any       =		train_dataset.select(range(32					)					)
						_lowercase					:       str       =		val_dataset.select(range(16					)					)

						_lowercase					:       Optional[Any]       =		4

						def _map_to_encoder_decoder_inputs(UpperCamelCase_				:	Dict					):
							# Tokenizer will automatically set [BOS] <text> [EOS]
							_lowercase					:       int       =		tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=UpperCamelCase_     ,  max_length=512					)
							_lowercase					:       List[str]       =		tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=UpperCamelCase_     ,  max_length=128					)
							_lowercase					:       Dict       =		inputs.input_ids
							_lowercase					:       str       =		inputs.attention_mask

							_lowercase					:       List[Any]       =		outputs.input_ids
							_lowercase					:       Optional[Any]       =		outputs.input_ids.copy()
							_lowercase					:       Optional[int]       =		[
							    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
							]
							_lowercase					:       Any       =		outputs.attention_mask

							assert all(len(UpperCamelCase_					) == 512 for x in inputs.input_ids					)
							assert all(len(UpperCamelCase_					) == 128 for x in outputs.input_ids					)

							return batch

						def _compute_metrics(UpperCamelCase_				:	Union[str, Any]					):
							_lowercase					:       int       =		pred.label_ids
							_lowercase					:       Tuple       =		pred.predictions

							# all unnecessary tokens are removed
							_lowercase					:       Optional[int]       =		tokenizer.batch_decode(UpperCamelCase_     ,  skip_special_tokens=UpperCamelCase_					)
							_lowercase					:       List[Any]       =		tokenizer.batch_decode(UpperCamelCase_     ,  skip_special_tokens=UpperCamelCase_					)

							_lowercase					:       Tuple       =		sum([int(pred_str[i] == label_str[i]					) for i in range(len(UpperCamelCase_					)					)]					) / len(UpperCamelCase_					)

							return {"accuracy": accuracy}

						# map train dataset
						_lowercase					:       Union[str, Any]       =		train_dataset.map(
						    _map_to_encoder_decoder_inputs     ,  batched=UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  remove_columns=['article', 'highlights']     ,  )
						train_dataset.set_format(
						    type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

						# same for validation dataset
						_lowercase					:       Union[str, Any]       =		val_dataset.map(
						    _map_to_encoder_decoder_inputs     ,  batched=UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  remove_columns=['article', 'highlights']     ,  )
						val_dataset.set_format(
						    type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

						_lowercase					:       Dict       =		self.get_auto_remove_tmp_dir()

						_lowercase					:       Union[str, Any]       =		SeqaSeqTrainingArguments(
						    output_dir=UpperCamelCase_     ,  per_device_train_batch_size=UpperCamelCase_     ,  per_device_eval_batch_size=UpperCamelCase_     ,  predict_with_generate=UpperCamelCase_     ,  evaluation_strategy='steps'     ,  do_train=UpperCamelCase_     ,  do_eval=UpperCamelCase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

						# instantiate trainer
						_lowercase					:       List[Any]       =		SeqaSeqTrainer(
						    model=UpperCamelCase_     ,  args=UpperCamelCase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=UpperCamelCase_     ,  eval_dataset=UpperCamelCase_     ,  tokenizer=UpperCamelCase_     ,  )

						# start training
						trainer.train()





'''simple docstring'''

from collections import UserDict
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_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_A				:    Optional[int]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  **UpperCamelCase_				:	List[str]					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
