


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class     UpperCAmelCase__ (   unittest.TestCase		):



     '''simple docstring'''




     def 	lowerCAmelCase_ (			self							:							Dict		):





            """simple docstring"""



            _lowercase       :					Dict     							=      0




     def 	lowerCAmelCase_ (			self							:							Any		):





            """simple docstring"""



            _lowercase       :					Optional[Any]     							=      AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
            self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							Optional[Any]		):





            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowercase       :					Tuple     							=      Path(UpperCAmelCase__		) / '''preprocessor_config.json'''
                   _lowercase       :					List[Any]     							=      Path(UpperCAmelCase__		) / '''config.json'''
                   json.dump(
                       {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}						,   open(UpperCAmelCase__						,   '''w'''		)						,   )
                   json.dump({'''model_type''': '''clip'''}						,   open(UpperCAmelCase__						,   '''w'''		)		)

                   _lowercase       :					Optional[Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		)
                   self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							List[Any]		):





            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowercase       :					Optional[Any]     							=      Path(UpperCAmelCase__		) / '''preprocessor_config.json'''
                   _lowercase       :					List[Any]     							=      Path(UpperCAmelCase__		) / '''config.json'''
                   json.dump(
                       {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}						,   open(UpperCAmelCase__						,   '''w'''		)						,   )
                   json.dump({'''model_type''': '''clip'''}						,   open(UpperCAmelCase__						,   '''w'''		)		)

                   _lowercase       :					Union[str, Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		)
                   self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							Optional[Any]		):





            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowercase       :					Any     							=      CLIPConfig()

                   # Create a dummy config file with image_proceesor_type
                   _lowercase       :					int     							=      Path(UpperCAmelCase__		) / '''preprocessor_config.json'''
                   _lowercase       :					Any     							=      Path(UpperCAmelCase__		) / '''config.json'''
                   json.dump(
                       {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}						,   open(UpperCAmelCase__						,   '''w'''		)						,   )
                   json.dump({'''model_type''': '''clip'''}						,   open(UpperCAmelCase__						,   '''w'''		)		)

                   # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                   _lowercase       :					List[Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		).to_dict()

                   config_dict.pop('''image_processor_type'''		)
                   _lowercase       :					str     							=      CLIPImageProcessor(**UpperCAmelCase__		)

                   # save in new folder
                   model_config.save_pretrained(UpperCAmelCase__		)
                   config.save_pretrained(UpperCAmelCase__		)

                   _lowercase       :					Optional[Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		)

                   # make sure private variable is not incorrectly saved
                   _lowercase       :					int     							=      json.loads(config.to_json_string()		)
                   self.assertTrue('''_processor_class''' not in dict_as_saved		)

            self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							List[Any]		):





            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowercase       :					Tuple     							=      Path(UpperCAmelCase__		) / '''preprocessor_config.json'''
                   json.dump(
                       {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}						,   open(UpperCAmelCase__						,   '''w'''		)						,   )

                   _lowercase       :					List[Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		)
                   self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							int		):





            """simple docstring"""



            with self.assertRaisesRegex(
                UpperCAmelCase__						,   '''clip-base is not a local folder and is not a valid model identifier'''		):
                   _lowercase       :					Optional[int]     							=      AutoImageProcessor.from_pretrained('''clip-base'''		)




     def 	lowerCAmelCase_ (			self							:							List[str]		):





            """simple docstring"""



            with self.assertRaisesRegex(
                UpperCAmelCase__						,   R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
                   _lowercase       :					Tuple     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__						,   revision='''aaaaaa'''		)




     def 	lowerCAmelCase_ (			self							:							Optional[int]		):





            """simple docstring"""



            with self.assertRaisesRegex(
                UpperCAmelCase__						,   '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''						,   ):
                   _lowercase       :					Union[str, Any]     							=      AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''		)




     def 	lowerCAmelCase_ (			self							:							List[str]		):





            """simple docstring"""



            with self.assertRaises(UpperCAmelCase__		):
                   _lowercase       :					Tuple     							=      AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(UpperCAmelCase__		):
                   _lowercase       :					Optional[Any]     							=      AutoImageProcessor.from_pretrained(
                       '''hf-internal-testing/test_dynamic_image_processor'''						,   trust_remote_code=UpperCAmelCase__		)

            _lowercase       :					Union[str, Any]     							=      AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''						,   trust_remote_code=UpperCAmelCase__		)
            self.assertEqual(image_processor.__class__.__name__						,   '''NewImageProcessor'''		)

            # Test image processor can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(UpperCAmelCase__		)
                   _lowercase       :					Union[str, Any]     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__						,   trust_remote_code=UpperCAmelCase__		)
            self.assertEqual(reloaded_image_processor.__class__.__name__						,   '''NewImageProcessor'''		)




     def 	lowerCAmelCase_ (			self							:							Optional[Any]		):





            """simple docstring"""



            try:
                   AutoConfig.register('''custom'''						,   UpperCAmelCase__		)
                   AutoImageProcessor.register(UpperCAmelCase__						,   UpperCAmelCase__		)
                   # Trying to register something existing in the Transformers library will raise an error
                   with self.assertRaises(UpperCAmelCase__		):
                          AutoImageProcessor.register(UpperCAmelCase__						,   UpperCAmelCase__		)

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          _lowercase       :					List[Any]     							=      Path(UpperCAmelCase__		) / '''preprocessor_config.json'''
                          _lowercase       :					Tuple     							=      Path(UpperCAmelCase__		) / '''config.json'''
                          json.dump(
                              {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}						,   open(UpperCAmelCase__						,   '''w'''		)						,   )
                          json.dump({'''model_type''': '''clip'''}						,   open(UpperCAmelCase__						,   '''w'''		)		)

                          _lowercase       :					Tuple     							=      CustomImageProcessor.from_pretrained(UpperCAmelCase__		)

                   # Now that the config is registered, it can be used as any other config with the auto-API
                   with tempfile.TemporaryDirectory() as tmp_dir:
                          image_processor.save_pretrained(UpperCAmelCase__		)
                          _lowercase       :					int     							=      AutoImageProcessor.from_pretrained(UpperCAmelCase__		)
                          self.assertIsInstance(UpperCAmelCase__						,   UpperCAmelCase__		)

            finally:
                   if "custom" in CONFIG_MAPPING._extra_content:
                          del CONFIG_MAPPING._extra_content["custom"]
                   if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                          del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




     def 	lowerCAmelCase_ (			self							:							Tuple		):





            """simple docstring"""



            class     UpperCAmelCase__ (   UpperCAmelCase__		):



                 '''simple docstring'''
                 UpperCAmelCase_       =	True

            try:
                   AutoConfig.register('''custom'''						,   UpperCAmelCase__		)
                   AutoImageProcessor.register(UpperCAmelCase__						,   UpperCAmelCase__		)
                   # If remote code is not set, the default is to use local
                   _lowercase       :					str     							=      AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
                   self.assertEqual(image_processor.__class__.__name__						,   '''NewImageProcessor'''		)
                   self.assertTrue(image_processor.is_local		)

                   # If remote code is disabled, we load the local one.
                   _lowercase       :					Union[str, Any]     							=      AutoImageProcessor.from_pretrained(
                       '''hf-internal-testing/test_dynamic_image_processor'''						,   trust_remote_code=UpperCAmelCase__		)
                   self.assertEqual(image_processor.__class__.__name__						,   '''NewImageProcessor'''		)
                   self.assertTrue(image_processor.is_local		)

                   # If remote is enabled, we load from the Hub
                   _lowercase       :					List[str]     							=      AutoImageProcessor.from_pretrained(
                       '''hf-internal-testing/test_dynamic_image_processor'''						,   trust_remote_code=UpperCAmelCase__		)
                   self.assertEqual(image_processor.__class__.__name__						,   '''NewImageProcessor'''		)
                   self.assertTrue(not hasattr(UpperCAmelCase__						,   '''is_local'''		)		)

            finally:
                   if "custom" in CONFIG_MAPPING._extra_content:
                          del CONFIG_MAPPING._extra_content["custom"]
                   if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                          del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       return 1.0 / (1.0 + np.exp(-_outputs		))







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       np.max(_outputs					,							axis=-1					,							keepdims=lowercase_		)
       A__ 					=       np.exp(_outputs - maxes		)
       return shifted_exp / shifted_exp.sum(axis=-1					,							keepdims=lowercase_		)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''sigmoid'''
    UpperCAmelCase__				    = '''softmax'''
    UpperCAmelCase__				    = '''none'''


@add_end_docstrings(
    UpperCAmelCase__						,   R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''						,   )
class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = ClassificationFunction.NONE


    def __init__(     self :      Any   ,					**UpperCAmelCase__ :      Optional[Any])		->str:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           self.check_model_type(
               TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
               if self.framework == '''tf'''
               else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple=None   ,					UpperCAmelCase__ :      Any=None   ,					UpperCAmelCase__ :      int=""   ,					**UpperCAmelCase__ :      Any)		->int:





           '''simple docstring'''

           A__ 					=       tokenizer_kwargs

           A__ 					=       {}
           if hasattr(self.model.config   ,					'''return_all_scores''') and return_all_scores is None:
                  A__ 					=       self.model.config.return_all_scores

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) or top_k is None:
                  A__ 					=       top_k
                  A__ 					=       False
           elif return_all_scores is not None:
                  warnings.warn(
                      '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                      ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''   ,					UpperCAmelCase__   ,					)
                  if return_all_scores:
                         A__ 					=       None
                  else:
                         A__ 					=       1

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  A__ 					=       ClassificationFunction[function_to_apply.upper()]

           if function_to_apply is not None:
                  A__ 					=       function_to_apply
           return preprocess_params, {}, postprocess_params


    def __call__(     self :      str   ,					*UpperCAmelCase__ :      List[Any]   ,					**UpperCAmelCase__ :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       super().__call__(*UpperCAmelCase__   ,					**UpperCAmelCase__)
           # TODO try and retrieve it in a nicer way from _sanitize_parameters.
           A__ 					=       '''top_k''' not in kwargs
           if isinstance(args[0]   ,					UpperCAmelCase__) and _legacy:
                  # This pipeline is odd, and return a list when single item is run
                  return [result]
           else:
                  return result


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Any   ,					**UpperCAmelCase__ :      str)		->Dict[str, GenericTensor]:





           '''simple docstring'''

           A__ 					=       self.framework
           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  return self.tokenizer(**UpperCAmelCase__   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) and len(UpperCAmelCase__) == 1 and isinstance(inputs[0]   ,					UpperCAmelCase__) and len(inputs[0]) == 2:
                  # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                  return self.tokenizer(
                      text=inputs[0][0]   ,					text_pair=inputs[0][1]   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                  raise ValueError(
                      '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                      ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''')
           return self.tokenizer(UpperCAmelCase__   ,					return_tensors=UpperCAmelCase__   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple)		->Tuple:





           '''simple docstring'''

           return self.model(**UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Optional[int]=None   ,					UpperCAmelCase__ :      List[Any]=1   ,					UpperCAmelCase__ :      str=True)		->Dict:





           '''simple docstring'''

           if function_to_apply is None:
                  if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                         A__ 					=       ClassificationFunction.SIGMOID
                  elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                         A__ 					=       ClassificationFunction.SOFTMAX
                  elif hasattr(self.model.config   ,					'''function_to_apply''') and function_to_apply is None:
                         A__ 					=       self.model.config.function_to_apply
                  else:
                         A__ 					=       ClassificationFunction.NONE

           A__ 					=       model_outputs['''logits'''][0]
           A__ 					=       outputs.numpy()

           if function_to_apply == ClassificationFunction.SIGMOID:
                  A__ 					=       sigmoid(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.SOFTMAX:
                  A__ 					=       softmax(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.NONE:
                  A__ 					=       outputs
           else:
                  raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""")

           if top_k == 1 and _legacy:
                  return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

           A__ 					=       [
               {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(UpperCAmelCase__)
           ]
           if not _legacy:
                  dict_scores.sort(key=lambda UpperCAmelCase__: x["score"]   ,					reverse=UpperCAmelCase__)
                  if top_k is not None:
                         A__ 					=       dict_scores[:top_k]
           return dict_scores


