




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():
       import tensorflow as tf

       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
       from ..tf_utils import stable_softmax

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase_									=	logging.get_logger(__name__)





@add_end_docstrings(__lowerCamelCase)
class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[str]    ,       *UpperCamelCase_: Any    ,       **UpperCamelCase_: Union[str, Any]   ):
             super().__init__(*UpperCamelCase_    ,       **UpperCamelCase_   )
             requires_backends(self    ,       """vision"""   )
             self.check_model_type(
                 TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
                 if self.framework == """tf"""
                 else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Tuple=None   ):
             __lowerCamelCase   =						{}
             if top_k is not None:
                    __lowerCamelCase   =						top_k
             return {}, {}, postprocess_params
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str], "Image.Image", List["Image.Image"]]    ,       **UpperCamelCase_: Optional[int]   ):
             return super().__call__(UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						load_image(UpperCamelCase_   )
             __lowerCamelCase   =						self.image_processor(images=UpperCamelCase_    ,       return_tensors=self.framework   )
             return model_inputs
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						self.model(**UpperCamelCase_   )
             return model_outputs




      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Union[str, Any]=5   ):
             if top_k > self.model.config.num_labels:
                    __lowerCamelCase   =						self.model.config.num_labels

             if self.framework == "pt":
                    __lowerCamelCase   =						model_outputs.logits.softmax(-1   )[0]
                    __lowerCamelCase,   __lowerCamelCase   =						probs.topk(UpperCamelCase_   )
             elif self.framework == "tf":
                    __lowerCamelCase   =						stable_softmax(model_outputs.logits    ,       axis=-1   )[0]
                    __lowerCamelCase   =						tf.math.top_k(UpperCamelCase_    ,       k=UpperCamelCase_   )
                    __lowerCamelCase,   __lowerCamelCase   =						topk.values.numpy(), topk.indices.numpy()
             else:
                    raise ValueError(F'Unsupported framework: {self.framework}'   )

             __lowerCamelCase   =						scores.tolist()
             __lowerCamelCase   =						ids.tolist()
             return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_    ,       UpperCamelCase_   )]






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_snake_case		=  get_tests_dir("fixtures")



class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    mock.Mock()
														_lowerCAmelCase     :							int    =    500
														_lowerCAmelCase     :							Tuple    =    {}
														_lowerCAmelCase     :							str    =    HTTPError
														_lowerCAmelCase     :							Union[str, Any]    =    {}

														# Download this model to make sure it's in the cache.
														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
														# Under the mock environment we get a 500 error when trying to reach the model.
														with mock.patch("requests.Session.request",			return_value=__a) as mock_head:
																					_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
																					# This check we did call the fake head request
																					mock_head.assert_called()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained(
														    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")







							def    snake_case__      (   self):

														'''simple docstring'''

														with self.assertRaises(__a):
																					# config is in subfolder, the following should not work without specifying the subfolder
																					_lowerCAmelCase     :							int    =    AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")

														_lowerCAmelCase     :							Optional[Any]    =    AutoImageProcessor.from_pretrained(
														    "hf-internal-testing/stable-diffusion-all-variants",			subfolder="feature_extractor")

														self.assertIsNotNone(__a)



@is_staging_test
class   UpperCAmelCase_    (       unittest.TestCase):







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TOKEN
														HfFolder.save_token(__a)







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														try:
																					delete_repo(token=cls._token,			repo_id="test-image-processor")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="valid_org/test-image-processor-org")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="test-dynamic-image-processor")
														except HTTPError:
																					pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							str    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="test-image-processor",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("valid_org/test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="valid_org/test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="valid_org/test-image-processor-org",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														CustomImageProcessor.register_for_auto_class()
														_lowerCAmelCase     :							List[str]    =    CustomImageProcessor.from_pretrained(__a)

														image_processor.push_to_hub("test-dynamic-image-processor",			use_auth_token=self._token)

														# This has added the proper auto_map field to the config
														self.assertDictEqual(
														    image_processor.auto_map,			{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"},			)

														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(
														    f"{USER}/test-dynamic-image-processor",			trust_remote_code=__a)
														# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
														self.assertEqual(new_image_processor.__class__.__name__,			"CustomImageProcessor")
