"""simple docstring"""




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
     from PIL import Image
else:
     class 						__A							:






          @staticmethod
          def     __A  (		*a__				,     **a__  ):
            pass





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      str:
  _lowerCAmelCase						:       str  			=     hashlib.mda(image.tobytes()	)
  return m.hexdigest()[:10]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      Dict:
  _lowerCAmelCase						:       Dict  			=     np.array(_lowerCamelCase	)
  _lowerCAmelCase						:       Union[str, Any]  			=     npimg.shape
  return {"hash": hashimage(_lowerCamelCase	), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 						__A							( unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
     _UpperCamelCase							:			Union[str, Any]							=		dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     MaskGenerationPipeline(model=a__				,     image_processor=a__  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]






     def     __A  (		self				,     a__				,     a__  ):
       pass






     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF"""  )
     def     __A  (		self  ):
       pass






     @slow
     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     pipeline("""mask-generation"""				,     model="""facebook/sam-vit-huge"""  )

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       Dict  			=     []
       for i, o in enumerate(outputs["""masks"""]  ):
         new_outupt += [{"mask": mask_to_test_readable(a__  ), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(a__				,     decimals=4  )				,     [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9_9_6_7},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_9_3},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9_9_0_9},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9_8_7_9},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9_8_3_4},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9_7_1_6},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9_6_1_2},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9_5_9_9},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9_5_5_2},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9_5_3_2},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9_5_1_6},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9_4_9_9},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9_4_8_3},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9_4_6_4},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9_4_0_8},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9_3_3_5},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9_3_2_6},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9_2_6_2},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8_9_9_9},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8_9_8_6},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8_9_8_4},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8_8_7_3},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8_8_7_1}
           ]				,     )
       # fmt: on






     @require_torch
     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """facebook/sam-vit-huge"""
       _lowerCAmelCase						:       Any  			=     pipeline("""mask-generation"""				,     model=a__  )

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""				,     pred_iou_thresh=1				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       Tuple  			=     []
       for i, o in enumerate(outputs["""masks"""]  ):
         new_outupt += [{"mask": mask_to_test_readable(a__  ), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(a__				,     decimals=4  )				,     [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1_0},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
           ]				,     )





"""simple docstring"""




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_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




