

"""simple docstring"""



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

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


SCREAMING_SNAKE_CASE_					:	Any							  =						Dict[str, Any]
SCREAMING_SNAKE_CASE_					:	Tuple							  =						List[Prediction]



@add_end_docstrings(_lowerCamelCase				)
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Tuple    ,						*UpperCamelCase: str    ,						**UpperCamelCase: List[str]       ):






         """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 				UpperCamelCase	(     self: Optional[int]    ,						**UpperCamelCase: List[Any]       ):






         """simple docstring"""


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

    def __call__(     self: Union[str, Any]    ,						*UpperCamelCase: Any    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Any       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: int       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Tuple=0.9       ):






         """simple docstring"""


         A__										=  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.
              A__			,				A__										=  target_size[0].tolist()

              def unnormalize(UpperCamelCase: int       ):
                   return self._get_bounding_box(
                       torch.Tensor(
                           [
                               (width * bbox[0] / 10_00),
                               (height * bbox[1] / 10_00),
                               (width * bbox[2] / 10_00),
                               (height * bbox[3] / 10_00),
                           ]       )       )

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

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

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

         return annotation





    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: "torch.Tensor"       ):






         """simple docstring"""


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









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






