from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCamelCase__    							= {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__    							= ['GPTNeoXTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__    							= [
          'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTNeoXForCausalLM',
          'GPTNeoXForQuestionAnswering',
          'GPTNeoXForSequenceClassification',
          'GPTNeoXForTokenClassification',
          'GPTNeoXLayer',
          'GPTNeoXModel',
          'GPTNeoXPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neox import (
                GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoXForCausalLM,
                GPTNeoXForQuestionAnswering,
                GPTNeoXForSequenceClassification,
                GPTNeoXForTokenClassification,
                GPTNeoXLayer,
                GPTNeoXModel,
                GPTNeoXPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase__    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




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, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

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

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



