

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     _UpperCAmelCase			(    *A_  ,   **A_		):







															'''simple docstring'''


															pass

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Image	)				->    str:
				_UpperCAmelCase    :						List[str]							=      hashlib.mda(image.tobytes()	)
				return m.hexdigest()[:10]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Image	)				->    Dict:
				_UpperCAmelCase    :						Optional[Any]							=      np.array(lowerCAmelCase	)
				_UpperCAmelCase    :						Dict							=      npimg.shape
				return {"hash": hashimage(lowerCAmelCase	), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 			a					(		unittest.TestCase			):
						_lowercase        =     dict(
						    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
						_lowercase        =     dict(
						    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      MaskGenerationPipeline(model=A_  ,   image_processor=A_		)
										return image_segmenter, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


						@slow
						@require_torch
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      pipeline("mask-generation"  ,   model="facebook/sam-vit-huge"		)

										_UpperCAmelCase    :						Any							=      image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"  ,   points_per_batch=256		)

										# Shortening by hashing
										_UpperCAmelCase    :						Optional[int]							=      []
										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": 1.04_44},
										        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
										        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
										        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
										        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
										        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
										        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
										        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
										        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
										        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
										        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
										        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
										        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
										        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
										        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
										        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
										        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
										        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
										        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
										        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
										        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
										        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
										        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
										        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
										        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
										        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
										        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
										        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
										        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
										        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
										    ]  ,   )
										# fmt: on





						@require_torch
						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      "facebook/sam-vit-huge"
										_UpperCAmelCase    :						Any							=      pipeline("mask-generation"  ,   model=A_		)

										_UpperCAmelCase    :						str							=      image_segmenter(
										    "http://images.cocodataset.org/val2017/000000039769.jpg"  ,   pred_iou_thresh=1  ,   points_per_batch=256		)

										# Shortening by hashing
										_UpperCAmelCase    :						Union[str, Any]							=      []
										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": 1.04_44},
										        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
										        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
										        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
										        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
										    ]  ,   )





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


