





'''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, 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

lowercase_					= logging.get_logger(__name__)


@add_end_docstrings(snake_case_       )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		**A       )		->   List[Any]:
        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 ,		)		->   List[Any]:
        if "text_queries" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_queries"""       )

        if isinstance(A ,		(str, Image.Image)       ):
            _SCREAMING_SNAKE_CASE									=			{"""image""": image, """candidate_labels""": candidate_labels}
        else:
            _SCREAMING_SNAKE_CASE									=			image
        _SCREAMING_SNAKE_CASE									=			super().__call__(A ,		**A       )
        return results


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			{}
        if "threshold" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""threshold"""]
        if "top_k" in kwargs:
            _SCREAMING_SNAKE_CASE									=			kwargs["""top_k"""]
        return {}, {}, postprocess_params


    def 			snake_case_(		self ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			load_image(inputs["""image"""]       )
        _SCREAMING_SNAKE_CASE									=			inputs["""candidate_labels"""]
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			candidate_labels.split(""","""       )

        _SCREAMING_SNAKE_CASE									=			torch.tensor([[image.height, image.width]] ,		dtype=torch.intaa       )
        for i, candidate_label in enumerate(A       ):
            _SCREAMING_SNAKE_CASE									=			self.tokenizer(A ,		return_tensors=self.framework       )
            _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""target_size"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""candidate_label"""       )
        _SCREAMING_SNAKE_CASE									=			model_inputs.pop("""is_last"""       )

        _SCREAMING_SNAKE_CASE									=			self.model(**A       )

        _SCREAMING_SNAKE_CASE									=			{"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
        return model_outputs


    def 			snake_case_(		self ,		A ,		A=0.1 ,		A=None       )		->   str:
        _SCREAMING_SNAKE_CASE									=			[]
        for model_output in model_outputs:
            _SCREAMING_SNAKE_CASE									=			model_output["""candidate_label"""]
            _SCREAMING_SNAKE_CASE									=			BaseModelOutput(A       )
            _SCREAMING_SNAKE_CASE									=			self.image_processor.post_process_object_detection(
                outputs=A ,		threshold=A ,		target_sizes=model_output["""target_size"""]       )[0]

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

                _SCREAMING_SNAKE_CASE									=			{"""score""": score, """label""": label, """box""": box}
                results.append(A       )

        _SCREAMING_SNAKE_CASE									=			sorted(A ,		key=lambda A       : x["score"] ,		reverse=A       )
        if top_k:
            _SCREAMING_SNAKE_CASE									=			results[:top_k]

        return results





    def 			snake_case_(		self ,		A       )		->   Dict[str, int]:
        if self.framework != "pt":
            raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			box.int().tolist()
        _SCREAMING_SNAKE_CASE									=			{
            """xmin""": xmin,
            """ymin""": ymin,
            """xmax""": xmax,
            """ymax""": ymax,
        }
        return bbox








'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		gather(_lowercase		)
							assert gathered_tensor.tolist() == list(range(1	,				state.num_processes**2 + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Any							     =		[state.process_index]
							UpperCAmelCase     :		Union[str, Any]							     =		gather_object(_lowercase		)
							assert len(_lowercase		) == state.num_processes, F'''{gathered_obj}, {len(_lowercase		)} != {state.num_processes}'''
							assert gathered_obj == list(range(state.num_processes		)		), F'''{gathered_obj} != {list(range(state.num_processes		)		)}'''



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Optional[int]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		broadcast(_lowercase		)
							assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
							assert broadcasted_tensor.tolist() == list(range(1	,				state.num_processes + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# We need to pad the tensor with one more element if we are the main process
							# to ensure that we can pad
							if state.is_main_process:
														UpperCAmelCase     :		Optional[Any]							     =		torch.arange(state.num_processes + 1		).to(state.device		)
							else:
														UpperCAmelCase     :		Tuple							     =		torch.arange(state.num_processes		).to(state.device		)
							UpperCAmelCase     :		Optional[Any]							     =		pad_across_processes(_lowercase		)
							assert padded_tensor.shape == torch.Size([state.num_processes + 1]		)
							if not state.is_main_process:
														assert padded_tensor.tolist() == list(range(0	,				state.num_processes		)		) + [0]



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Optional[Any]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		reduce(_lowercase	,				"""sum"""		)
							UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([4.0, 6]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Tuple							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		reduce(_lowercase	,				"""mean"""		)
							UpperCAmelCase     :		str							     =		torch.tensor([2.0, 3]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							# For xla_spawn (TPUs)
							main()



def       __lowerCamelCase  (					)      ->			int:
							UpperCAmelCase     :		List[Any]							     =		PartialState()
							state.print(F'''State: {state}'''		)
							state.print("""testing gather"""		)
							test_gather(_lowercase		)
							state.print("""testing gather_object"""		)
							test_gather_object(_lowercase		)
							state.print("""testing broadcast"""		)
							test_broadcast(_lowercase		)
							state.print("""testing pad_across_processes"""		)
							test_pad_across_processes(_lowercase		)
							state.print("""testing reduce_sum"""		)
							test_reduce_sum(_lowercase		)
							state.print("""testing reduce_mean"""		)
							test_reduce_mean(_lowercase		)


if __name__ == "__main__":
							main()

