


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

__snake_case							   =  logging.get_logger(__name__)


__snake_case							   =  Dict[str, Any]
__snake_case							   =  List[Prediction]
@add_end_docstrings(__UpperCAmelCase      )
class      lowercase			(							__UpperCAmelCase      ):

		"""simple docstring"""

		def __init__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(*lowerCAmelCase_					,     **lowerCAmelCase_							)

							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 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				{}
							if "threshold" in kwargs:
												UpperCamelCase__     :Tuple							      =				kwargs['''threshold''']
							return {}, {}, postprocess_kwargs




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				load_image(lowerCAmelCase_							)
							UpperCamelCase__     :int							      =				torch.IntTensor([[image.height, image.width]]							)
							UpperCamelCase__     :List[Any]							      =				self.image_processor(images=[image]					,     return_tensors='''pt'''							)
							if self.tokenizer is not None:
												UpperCamelCase__     :Optional[int]							      =				self.tokenizer(text=inputs['''words''']					,     boxes=inputs['''boxes''']					,     return_tensors='''pt'''							)
							UpperCamelCase__     :List[Any]							      =				target_size
							return inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				model_inputs.pop('''target_size'''							)
							UpperCamelCase__     :int							      =				self.model(**lowerCAmelCase_							)
							UpperCamelCase__     :int							      =				outputs.__class__({'''target_size''': target_size, **outputs}							)
							if self.tokenizer is not None:
												UpperCamelCase__     :Tuple							      =				model_inputs['''bbox''']
							return model_outputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0.9							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				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__     :int							      =				target_size[0].tolist()

												def unnormalize(UpperCamelCase_							):
																	return self._get_bounding_box(
																	    torch.Tensor(
																	        [
																	            (width * bbox[0] / 1000),
																	            (height * bbox[1] / 1000),
																	            (width * bbox[2] / 1000),
																	            (height * bbox[3] / 1000),
																	        ]							)							)

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

												UpperCamelCase__     :Any							      =				scores.tolist()
												UpperCamelCase__     :Optional[int]							      =				[self.model.config.idalabel[label.item()] for label in labels]
												UpperCamelCase__     :Union[str, Any]							      =				[self._get_bounding_box(lowerCAmelCase_							) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												UpperCamelCase__     :Any							      =				['''score''', '''label''', '''box''']
												UpperCamelCase__     :int							      =				[
												    dict(zip(lowerCAmelCase_					,     lowerCAmelCase_							)							)
												    for vals in zip(raw_annotation['''scores''']					,     raw_annotation['''labels''']					,     raw_annotation['''boxes''']							)
												]

							return annotation




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)