






import copy
import os
from typing import Union

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


__lowerCamelCase	:		Optional[Any]										=   logging.get_logger(__name__)

__lowerCamelCase	:		List[str]										=   {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}







class  _lowercase   (   _A						):
      _a							:						Optional[Any]										=							'pix2struct_text_model'
      _a							:						int										=							['past_key_values']
      _a							:						List[str]										=							{
          'hidden_size': 'hidden_size',
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(     self				,			a=5_0_2_4_4				,			a=7_6_8				,			a=6_4				,			a=2_0_4_8				,			a=1_2				,			a=1_2				,			a=3_2				,			a=1_2_8				,			a=0.1				,			a=1e-6				,			a=1.0				,			a="gelu_new"				,			a=0				,			a=False				,			a=0				,			a=1				,			a=False				,			a=True				,			**a				,			):
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    Optional[int]							       =hidden_size
       snake_case__  :    str							       =d_kv
       snake_case__  :    int							       =d_ff
       snake_case__  :    Optional[int]							       =num_layers
       snake_case__  :    int							       =num_heads
       snake_case__  :    int							       =relative_attention_num_buckets
       snake_case__  :    Optional[int]							       =relative_attention_max_distance
       snake_case__  :    int							       =dropout_rate
       snake_case__  :    List[str]							       =layer_norm_epsilon
       snake_case__  :    Optional[Any]							       =initializer_factor
       snake_case__  :    str							       =use_cache

       snake_case__  :    Optional[int]							       =eos_token_id
       snake_case__  :    List[Any]							       =decoder_start_token_id

       # for backwards compatibility
       snake_case__  :    Optional[Any]							       =dense_act_fn

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



      @classmethod
      def 						lowercase__					(     cls				,			a				,			**a   ):
       cls._set_token_in_kwargs(a   )

       snake_case__       ,   snake_case__  :    int							       =cls.get_config_dict(a				,			**a   )

       # get the text config dict if we are loading from Pix2StructConfig
       if config_dict.get("""model_type"""   ) == "pix2struct":
        snake_case__  :    List[str]							       =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(a				,			**a   )







class  _lowercase   (   _A						):
      _a							:						List[str]										=							'pix2struct_vision_model'
      def __init__(     self				,			a=7_6_8				,			a=7_6_8				,			a=2_0_4_8				,			a=6_4				,			a=1_2				,			a=1_2				,			a="gelu_new"				,			a=1e-6				,			a=0.0				,			a=0.0				,			a=1e-10				,			a=1.0				,			a=4_0_9_6				,			a=3_2				,			a=1_2_8				,			**a				,			):
       super().__init__(**a   )

       snake_case__  :    Tuple							       =hidden_size
       snake_case__  :    Union[str, Any]							       =patch_embed_hidden_size
       snake_case__  :    List[str]							       =d_ff
       snake_case__  :    Any							       =dropout_rate
       snake_case__  :    Tuple							       =num_hidden_layers
       snake_case__  :    Union[str, Any]							       =num_attention_heads
       snake_case__  :    List[Any]							       =initializer_range
       snake_case__  :    int							       =initializer_factor
       snake_case__  :    List[str]							       =attention_dropout
       snake_case__  :    Tuple							       =layer_norm_eps
       snake_case__  :    List[Any]							       =dense_act_fn
       snake_case__  :    List[Any]							       =seq_len
       snake_case__  :    Tuple							       =relative_attention_num_buckets
       snake_case__  :    Any							       =relative_attention_max_distance
       snake_case__  :    Union[str, Any]							       =d_kv



      @classmethod
      def 						lowercase__					(     cls				,			a				,			**a   ):
       cls._set_token_in_kwargs(a   )

       snake_case__       ,   snake_case__  :    int							       =cls.get_config_dict(a				,			**a   )

       # get the vision config dict if we are loading from Pix2StructConfig
       if config_dict.get("""model_type"""   ) == "pix2struct":
        snake_case__  :    Any							       =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(a				,			**a   )







class  _lowercase   (   _A						):
      _a							:						List[str]										=							'pix2struct'
      _a							:						str										=							True
      def __init__(     self				,			a=None				,			a=None				,			a=1.0				,			a=0.02				,			a=False				,			a=False				,			a=True				,			**a				,			):
       super().__init__(tie_word_embeddings=a				,			is_encoder_decoder=a				,			**a   )

       if text_config is None:
        snake_case__  :    Optional[int]							       ={}
        logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""   )

       if vision_config is None:
        snake_case__  :    Dict							       ={}
        logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""   )

       snake_case__  :    Union[str, Any]							       =PixaStructTextConfig(**a   )
       snake_case__  :    Optional[int]							       =PixaStructVisionConfig(**a   )

       snake_case__  :    List[Any]							       =self.text_config.decoder_start_token_id
       snake_case__  :    Union[str, Any]							       =self.text_config.pad_token_id
       snake_case__  :    Union[str, Any]							       =self.text_config.eos_token_id

       snake_case__  :    Optional[int]							       =initializer_factor
       snake_case__  :    int							       =initializer_range

       snake_case__  :    Any							       =self.initializer_range
       snake_case__  :    List[Any]							       =self.initializer_range

       snake_case__  :    List[Any]							       =is_vqa
      @classmethod
      def 						lowercase__					(     cls				,			a				,			a				,			**a   ):
       return cls(text_config=text_config.to_dict()				,			vision_config=vision_config.to_dict()				,			**a   )



      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =copy.deepcopy(self.__dict__   )
       snake_case__  :    Tuple							       =self.text_config.to_dict()
       snake_case__  :    Optional[Any]							       =self.vision_config.to_dict()
       snake_case__  :    Any							       =self.__class__.model_type
       return output













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)







@add_end_docstrings(_A						)
class  _lowercase   (   _A						):
      def __init__(     self				,			**a   ):
       super().__init__(**a   )

       if self.framework == "tf":
        raise ValueError(F"The {self.__class__} is only available in PyTorch."   )

       requires_backends(self				,			"""vision"""   )
       self.check_model_type(a   )
      def __call__(     self				,			a				,			a = None				,			**a				,			):
       if "text_queries" in kwargs:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =self.image_processor(a				,			return_tensors=self.framework   )
        yield {
            "is_last": i == len(a   ) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

        for index in outputs["scores"].nonzero():
         snake_case__  :    int							       =outputs["""scores"""][index].item()
         snake_case__  :    Tuple							       =self._get_bounding_box(outputs["""boxes"""][index][0]   )

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



      def 						lowercase__					(     self				,			a   ):
       if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""   )
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =box.int().tolist()
       snake_case__  :    int							       ={
           """xmin""": xmin,
           """ymin""": ymin,
           """xmax""": xmax,
           """ymax""": ymax,
       }
       return bbox





