
"""simple docstring"""




import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   parent
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   config_class
											SCREAMING_SNAKE_CASE__						:       int           =   has_text_modality
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   kwargs
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   common_properties



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.config_class(**self.inputs_dict    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   (
											    ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
											    if self.common_properties is None
											    else self.common_properties
											)

											# Add common fields for text models
											if self.has_text_modality:
																		common_properties.extend(["""vocab_size"""]    )

											# Test that config has the common properties as getters
											for prop in common_properties:
																		self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )   ,       msg=F'''`{prop}` does not exist'''    )

											# Test that config has the common properties as setter
											for idx, name in enumerate(SCREAMING_SNAKE_CASE__    ):
																		try:
																									setattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									self.parent.assertEqual(
																									    getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__   ,       msg=F'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )}'''    )
																		except NotImplementedError:
																									# Some models might not be able to implement setters for common_properties
																									# In that case, a NotImplementedError is raised
																									pass

        # Test if config class can be called with Config(prop_name=..)
											for idx, name in enumerate(SCREAMING_SNAKE_CASE__    ):
																		try:
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   self.config_class(**{name: idx}    )
																									self.parent.assertEqual(
																									    getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__   ,       msg=F'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )}'''    )
																		except NotImplementedError:
																									# Some models might not be able to implement setters for common_properties
																									# In that case, a NotImplementedError is raised
																									pass



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.config_class(**self.inputs_dict    )
											SCREAMING_SNAKE_CASE__						:       Any           =   json.loads(config.to_json_string()    )
											for key, value in self.inputs_dict.items():
																		self.parent.assertEqual(obj[key]   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.config_class(**self.inputs_dict    )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE__						:       Any           =   os.path.join(SCREAMING_SNAKE_CASE__   ,       """config.json"""    )
																		config_first.to_json_file(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       str           =   self.config_class.from_json_file(SCREAMING_SNAKE_CASE__    )

											self.parent.assertEqual(config_second.to_dict()   ,       config_first.to_dict()    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.config_class(**self.inputs_dict    )

											with tempfile.TemporaryDirectory() as tmpdirname:
																		config_first.save_pretrained(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__    )

											self.parent.assertEqual(config_second.to_dict()   ,       config_first.to_dict()    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.config_class(**self.inputs_dict    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """test"""
											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   os.path.join(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		config_first.save_pretrained(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__   ,       subfolder=SCREAMING_SNAKE_CASE__    )

											self.parent.assertEqual(config_second.to_dict()   ,       config_first.to_dict()    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.config_class(**self.inputs_dict   ,       num_labels=5    )
											self.parent.assertEqual(len(config.idalabel    )   ,       5    )
											self.parent.assertEqual(len(config.labelaid    )   ,       5    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   3
											self.parent.assertEqual(len(config.idalabel    )   ,       3    )
											self.parent.assertEqual(len(config.labelaid    )   ,       3    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											if self.config_class.is_composition:
																		return
											SCREAMING_SNAKE_CASE__						:       str           =   self.config_class()
											self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   copy.deepcopy(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.config_class(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   []
											for key, value in config_common_kwargs.items():
																		if key == "torch_dtype":
																									if not is_torch_available():
																																continue
																									else:
																																import torch

																																if config.torch_dtype != torch.floataa:
																																							wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)    )
																		elif getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) != value:
																									wrong_values.append((key, getattr(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ), value)    )

											if len(SCREAMING_SNAKE_CASE__    ) > 0:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]    )
																		raise ValueError(F'''The following keys were not properly set in the config:\n{errors}'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											self.create_and_test_config_common_properties()
											self.create_and_test_config_to_json_string()
											self.create_and_test_config_to_json_file()
											self.create_and_test_config_from_and_save_pretrained()
											self.create_and_test_config_from_and_save_pretrained_subfolder()
											self.create_and_test_config_with_num_labels()
											self.check_config_can_be_init_without_params()
											self.check_config_arguments_init()








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

SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           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 			UpperCamelCase     (					self,      lowerCamelCase=None)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		{}
           if top_k is not None:
               _lowercase       :							List[str]      		=		top_k
           return {}, {}, postprocess_params
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		load_image(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.model(**lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=5)       ->   Dict:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase       :							List[Any]      		=		self.model.config.num_labels

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

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


