




'''simple docstring'''



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       _UpperCamelCase			(_lowerCamelCase				:  Optional[int]						)-> Dict:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> List[Any]:




     '''simple docstring'''





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





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   int										=      '''sigmoid'''
       __lowercase	:   str										=      '''softmax'''
       __lowercase	:   int										=      '''none'''





@add_end_docstrings(
    __lowerCAmelCase					, 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      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[int]										=      False
       __lowercase	:   Dict										=      ClassificationFunction.NONE


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=""     ,	**__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	tokenizer_kwargs

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

            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) or top_k is None:
                 __snake_case            =	top_k
                 __snake_case            =	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`.'''     ,	__SCREAMING_SNAKE_CASE     ,	)
                 if return_all_scores:
                      __snake_case            =	None
                 else:
                      __snake_case            =	1

            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	ClassificationFunction[function_to_apply.upper()]

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


       def __call__( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Dict[str, GenericTensor]:



            '''simple docstring'''




            __snake_case            =	self.framework
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 return self.tokenizer(**__SCREAMING_SNAKE_CASE     ,	return_tensors=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            elif isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) == 1 and isinstance(inputs[0]     ,	__SCREAMING_SNAKE_CASE	) 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=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            elif isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 # 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(__SCREAMING_SNAKE_CASE     ,	return_tensors=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            return self.model(**__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=1     ,	__SCREAMING_SNAKE_CASE=True	)   ->	Optional[Any]:



            '''simple docstring'''




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

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

            if function_to_apply == ClassificationFunction.SIGMOID:
                 __snake_case            =	sigmoid(__SCREAMING_SNAKE_CASE	)
            elif function_to_apply == ClassificationFunction.SOFTMAX:
                 __snake_case            =	softmax(__SCREAMING_SNAKE_CASE	)
            elif function_to_apply == ClassificationFunction.NONE:
                 __snake_case            =	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()}

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










'''simple docstring'''
import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							A_			    =		MobileNetVaConfig(layer_norm_eps=0.001 )

							if "_quant" in model_name:
														raise ValueError("Quantized models are not supported." )

							A_			    =		re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"						,  UpperCamelCase_ )
							if matches:
														A_			    =		float(matches[1] )
														A_			    =		int(matches[2] )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							A_			    =		1_0_0_1
							A_			    =		"imagenet-1k-id2label.json"
							A_			    =		"huggingface/label-files"
							A_			    =		json.load(open(hf_hub_download(UpperCamelCase_						,  UpperCamelCase_						,  repo_type="dataset" )						,  "r" ) )
							A_			    =		{int(UpperCamelCase_ ) + 1: v for k, v in idalabel.items()}
							A_			    =		"background"
							A_			    =		idalabel
							A_			    =		{v: k for k, v in idalabel.items()}

							return config







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							A_			    =		get_mobilenet_va_config(UpperCamelCase_ )

							# Load 🤗 model
							A_			    =		MobileNetVaForImageClassification(UpperCamelCase_ ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A_			    =		MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}						,  size={"shortest_edge": config.image_size + 3_2}						,  )
							A_			    =		image_processor(images=prepare_img()						,  return_tensors="pt" )
							A_			    =		model(**UpperCamelCase_ )
							A_			    =		outputs.logits

							assert logits.shape == (1, 1_0_0_1)

							if model_name == "mobilenet_v1_1.0_224":
														A_			    =		torch.tensor([-4.1739, -1.1233, 3.1205] )
							elif model_name == "mobilenet_v1_0.75_192":
														A_			    =		torch.tensor([-3.9440, -2.3141, -0.3333] )
							else:
														A_			    =		None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]						,  UpperCamelCase_						,  atol=1e-4 )

							Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
							print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
							model.save_pretrained(UpperCamelCase_ )
							print(f"Saving image processor to {pytorch_dump_folder_path}" )
							image_processor.save_pretrained(UpperCamelCase_ )

							if push_to_hub:
														print("Pushing to the hub..." )
														A_			    =		"google/" + model_name
														image_processor.push_to_hub(UpperCamelCase_ )
														model.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				int									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''mobilenet_v1_1.0_224''',
							    type=str,
							    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)

							__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





