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(_a			)
class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )
            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 			a__  (					self				,      lowerCAmelCase__=None       ):
            _A=	{}
            if top_k is not None:
                   _A=	top_k
            return {}, {}, postprocess_params



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



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	load_image(lowerCAmelCase__       )
            _A=	self.image_processor(images=lowerCAmelCase__				,      return_tensors=self.framework       )
            return model_inputs



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	self.model(**lowerCAmelCase__       )
            return model_outputs





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=5       ):
            if top_k > self.model.config.num_labels:
                   _A=	self.model.config.num_labels

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

            _A=	scores.tolist()
            _A=	ids.tolist()
            return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__				,      lowerCAmelCase__       )]
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






       _A=	tempfile.mkdtemp()
       return os.path.join(lowerCAmelCase_       ,					str(uuid.uuida()   ) + suffix   )





@require_soundfile
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )

            del agent_type

            # Ensure the path remains even after the object deletion
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )

            # Ensure that the file contains the same value as the original tensor
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )
            self.assertEqual(agent_type.to_string()				,      lowerCAmelCase__       )





@require_vision
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type._tensor				,      atol=1E-4       )       )

            self.assertIsInstance(agent_type.to_raw()				,      Image.Image       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertTrue(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertFalse(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      agent_type.to_string()       )
            self.assertEqual(lowerCAmelCase__				,      agent_type.to_raw()       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )