




"""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()
lowerCAmelCase							:			str     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Optional[Any]:
			lowerCamelCase			      =					MobileNetVaConfig(layer_norm_eps=0.001				)

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

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

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			lowerCamelCase			      =					10_01
			lowerCamelCase			      =					"""imagenet-1k-id2label.json"""
			lowerCamelCase			      =					"""huggingface/label-files"""
			lowerCamelCase			      =					json.load(open(hf_hub_download(snake_case_       , snake_case_       , repo_type="""dataset"""				)       , """r"""				)				)
			lowerCamelCase			      =					{int(snake_case_				) + 1: v for k, v in idalabel.items()}
			lowerCamelCase			      =					"""background"""
			lowerCamelCase			      =					idalabel
			lowerCamelCase			      =					{v: k for k, v in idalabel.items()}

			return config



def     a__ (				)						->					Dict:
			lowerCamelCase			      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase			      =					Image.open(requests.get(snake_case_       , stream=snake_case_				).raw				)
			return im



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__=False				)						->					List[Any]:
			lowerCamelCase			      =					get_mobilenet_va_config(snake_case_				)

			# Load 🤗 model
			lowerCamelCase			      =					MobileNetVaForImageClassification(snake_case_				).eval()

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

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			lowerCamelCase			      =					MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}       , size={"""shortest_edge""": config.image_size + 32}       , )
			lowerCamelCase			      =					image_processor(images=prepare_img()       , return_tensors="""pt"""				)
			lowerCamelCase			      =					model(**snake_case_				)
			lowerCamelCase			      =					outputs.logits

			assert logits.shape == (1, 10_01)

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

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

			Path(snake_case_				).mkdir(exist_ok=snake_case_				)
			print(F'Saving model {model_name} to {pytorch_dump_folder_path}'				)
			model.save_pretrained(snake_case_				)
			print(F'Saving image processor to {pytorch_dump_folder_path}'				)
			image_processor.save_pretrained(snake_case_				)

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


if __name__ == "__main__":
					lowerCAmelCase							:			List[Any]     							=							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."""
					)

					lowerCAmelCase							:			Tuple     							=							parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)




'''simple docstring'''





from typing import Dict

import numpy as np

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


if is_tf_available():
    import tensorflow as tf

    from ..tf_utils import stable_softmax


if is_torch_available():
    import torch


SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)




@add_end_docstrings(
    UpperCamelCase__		,	r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """		,	)
class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    str   ,			__a    :    GenericTensor ):
         if self.framework == "tf":
              UpperCAmelCase_           =   tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
         elif self.framework == "pt":
              UpperCAmelCase_           =   torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,			as_tuple=__a )
         else:
              raise ValueError("Unsupported framework" )
         return masked_index



    def 					_lowercase						(self    :    Tuple   ,			__a    :    GenericTensor ):
         UpperCAmelCase_           =   self.get_masked_index(__a )
         UpperCAmelCase_           =   np.prod(masked_index.shape )
         if numel < 1:
              raise PipelineException(
                  "fill-mask"   ,			self.model.base_model_prefix   ,			f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""   ,			)



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    GenericTensor ):
         if isinstance(__a   ,			__a ):
              for model_input in model_inputs:
                   self._ensure_exactly_one_mask_token(model_input["input_ids"][0] )
         else:
              for input_ids in model_inputs["input_ids"]:
                   self._ensure_exactly_one_mask_token(__a )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict   ,			__a    :    List[str]=None   ,			**__a    :    Any ):
         if return_tensors is None:
              UpperCAmelCase_           =   self.framework
         UpperCAmelCase_           =   self.tokenizer(__a   ,			return_tensors=__a )
         self.ensure_exactly_one_mask_token(__a )
         return model_inputs



    def 					_lowercase						(self    :    str   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   self.model(**__a )
         UpperCAmelCase_           =   model_inputs["input_ids"]
         return model_outputs



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Tuple   ,			__a    :    int=5   ,			__a    :    Dict=None ):
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
              UpperCAmelCase_           =   target_ids.shape[0]
         UpperCAmelCase_           =   model_outputs["input_ids"][0]
         UpperCAmelCase_           =   model_outputs["logits"]

         if self.framework == "tf":
              UpperCAmelCase_           =   tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

              UpperCAmelCase_           =   outputs.numpy()

              UpperCAmelCase_           =   outputs[0, masked_index, :]
              UpperCAmelCase_           =   stable_softmax(__a   ,			axis=-1 )
              if target_ids is not None:
                   UpperCAmelCase_           =   tf.gather_nd(tf.squeeze(__a   ,			0 )   ,			target_ids.reshape(-1   ,			1 ) )
                   UpperCAmelCase_           =   tf.expand_dims(__a   ,			0 )

              UpperCAmelCase_           =   tf.math.top_k(__a   ,			k=__a )
              UpperCAmelCase_    , UpperCAmelCase_           =   topk.values.numpy(), topk.indices.numpy()
         else:
              UpperCAmelCase_           =   torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,			as_tuple=__a ).squeeze(-1 )
              # Fill mask pipeline supports only one ${mask_token} per sample

              UpperCAmelCase_           =   outputs[0, masked_index, :]
              UpperCAmelCase_           =   logits.softmax(dim=-1 )
              if target_ids is not None:
                   UpperCAmelCase_           =   probs[..., target_ids]

              UpperCAmelCase_    , UpperCAmelCase_           =   probs.topk(__a )

         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()   ,			predictions.tolist() ) ):
              UpperCAmelCase_           =   []
              for v, p in zip(_values   ,			_predictions ):
                   # Copy is important since we're going to modify this array in place
                   UpperCAmelCase_           =   input_ids.numpy().copy()
                   if target_ids is not None:
                        UpperCAmelCase_           =   target_ids[p].tolist()

                   UpperCAmelCase_           =   p
                   # Filter padding out:
                   UpperCAmelCase_           =   tokens[np.where(tokens != self.tokenizer.pad_token_id )]
                   # Originally we skip special tokens to give readable output.
                   # For multi masks though, the other [MASK] would be removed otherwise
                   # making the output look odd, so we add them back
                   UpperCAmelCase_           =   self.tokenizer.decode(__a   ,			skip_special_tokens=__a )
                   UpperCAmelCase_           =   {"score": v, "token": p, "token_str": self.tokenizer.decode([p] ), "sequence": sequence}
                   row.append(__a )
              result.append(__a )
         if single_mask:
              return result[0]
         return result



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[Any]   ,			__a    :    List[str]=None ):
         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   [targets]
         try:
              UpperCAmelCase_           =   self.tokenizer.get_vocab()
         except Exception:
              UpperCAmelCase_           =   {}
         UpperCAmelCase_           =   []
         for target in targets:
              UpperCAmelCase_           =   vocab.get(__a   ,			__a )
              if id_ is None:
                   UpperCAmelCase_           =   self.tokenizer(
                       __a   ,			add_special_tokens=__a   ,			return_attention_mask=__a   ,			return_token_type_ids=__a   ,			max_length=1   ,			truncation=__a   ,			)["input_ids"]
                   if len(__a ) == 0:
                        logger.warning(
                            f"""The specified target token `{target}` does not exist in the model vocabulary. """
                            "We cannot replace it with anything meaningful, ignoring it" )
                        continue
                   UpperCAmelCase_           =   input_ids[0]
                   # XXX: If users encounter this pass
                   # it becomes pretty slow, so let's make sure
                   # The warning enables them to fix the input to
                   # get faster performance.
                   logger.warning(
                       f"""The specified target token `{target}` does not exist in the model vocabulary. """
                       f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
              target_ids.append(id_ )
         UpperCAmelCase_           =   list(set(__a ) )
         if len(__a ) == 0:
              raise ValueError("At least one target must be provided when passed." )
         UpperCAmelCase_           =   np.array(__a )
         return target_ids



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict=None   ,			__a    :    List[str]=None ):
         UpperCAmelCase_           =   {}

         if targets is not None:
              UpperCAmelCase_           =   self.get_target_ids(__a   ,			__a )
              UpperCAmelCase_           =   target_ids

         if top_k is not None:
              UpperCAmelCase_           =   top_k

         if self.tokenizer.mask_token_id is None:
              raise PipelineException(
                  "fill-mask"   ,			self.model.base_model_prefix   ,			"The tokenizer does not define a `mask_token`." )
         return {}, {}, postprocess_params






    def __call__(self    :    Union[str, Any]   ,			__a    :    str   ,			*__a    :    Any   ,			**__a    :    Tuple ):
         UpperCAmelCase_           =   super().__call__(__a   ,			**__a )
         if isinstance(__a   ,			__a ) and len(__a ) == 1:
              return outputs[0]
         return outputs




