





from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
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_VISUAL_QUESTION_ANSWERING_MAPPING

_a 						=      logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase )
class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def __init__(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(*__lowerCAmelCase       ,  **__lowerCAmelCase							)
				self.check_model_type(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		{}, {}
				if padding is not None:
					lowerCamelCase__								=		padding
				if truncation is not None:
					lowerCamelCase__								=		truncation
				if top_k is not None:
					lowerCamelCase__								=		top_k
				return preprocess_params, {}, postprocess_params





			def __call__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase       ,  (Image.Image, str)							) and isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		{'''image''': image, '''question''': question}
				else:
					lowerCamelCase__								=		image
				lowerCamelCase__								=		super().__call__(__lowerCAmelCase       ,  **__lowerCAmelCase							)
				return results





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				lowerCamelCase__								=		load_image(inputs['''image''']							)
				lowerCamelCase__								=		self.tokenizer(
				    inputs['''question''']       ,  return_tensors=self.framework       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase							)
				lowerCamelCase__								=		self.image_processor(images=__lowerCAmelCase       ,  return_tensors=self.framework							)
				model_inputs.update(__lowerCAmelCase							)
				return model_inputs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model(**__lowerCAmelCase							)
				return model_outputs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=5							):



				'''simple docstring'''







				if top_k > self.model.config.num_labels:
					lowerCamelCase__								=		self.model.config.num_labels

				if self.framework == "pt":
					lowerCamelCase__								=		model_outputs.logits.sigmoid()[0]
					lowerCamelCase__				,	lowerCamelCase__								=		probs.topk(__lowerCAmelCase							)
				else:
					raise ValueError(F'Unsupported framework: {self.framework}'							)

				lowerCamelCase__								=		scores.tolist()
				lowerCamelCase__								=		ids.tolist()
				return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCAmelCase       ,  __lowerCAmelCase							)]










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(pad_token_id=__lowerCAmelCase       ,  bos_token_id=__lowerCAmelCase       ,  eos_token_id=__lowerCAmelCase       ,  **__lowerCAmelCase							)

				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		position_embedding_type
				lowerCamelCase__								=		use_cache
				lowerCamelCase__								=		classifier_dropout






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				if self.task == "multiple-choice":
					lowerCamelCase__								=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
					lowerCamelCase__								=		{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]							)



