




'''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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
a		:  Any   			=			logging.get_logger(__name__)





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       Optional[int]       					=			MobileNetVaConfig(layer_norm_eps=0.001	)

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

    _a							:       Optional[int]       					=			re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""      ,  UpperCAmelCase_	)
    if matches:
        _a							:       Tuple       					=			float(matches[1]	)
        _a							:       List[str]       					=			int(matches[2]	)

    if model_name.startswith("""deeplabv3_"""	):
        _a							:       int       					=			8
        _a							:       str       					=			21
        _a							:       int       					=			"""pascal-voc-id2label.json"""
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        _a							:       int       					=			1_001
        _a							:       Optional[Any]       					=			"""imagenet-1k-id2label.json"""

    _a							:       List[str]       					=			"""huggingface/label-files"""
    _a							:       int       					=			json.load(open(hf_hub_download(UpperCAmelCase_      ,  UpperCAmelCase_      ,  repo_type="""dataset"""	)      ,  """r"""	)	)

    if config.num_labels == 1_001:
        _a							:       Any       					=			{int(UpperCAmelCase_	) + 1: v for k, v in idalabel.items()}
        _a							:       str       					=			"""background"""
    else:
        _a							:       Any       					=			{int(UpperCAmelCase_	): v for k, v in idalabel.items()}

    _a							:       Optional[Any]       					=			idalabel
    _a							:       Optional[Any]       					=			{v: k for k, v in idalabel.items()}

    return config





def UpperCAmelCase						(					):
    _a							:       Union[str, Any]       					=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _a							:       Dict       					=			Image.open(requests.get(UpperCAmelCase_      ,  stream=UpperCAmelCase_	).raw	)
    return im





@torch.no_grad()
def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_=False	):
    _a							:       Optional[int]       					=			get_mobilenet_va_config(UpperCAmelCase_	)

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""	):
        _a							:       Optional[int]       					=			MobileNetVaForSemanticSegmentation(UpperCAmelCase_	).eval()
    else:
        _a							:       Optional[int]       					=			MobileNetVaForImageClassification(UpperCAmelCase_	).eval()

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

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    _a							:       str       					=			MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size}      ,  size={"""shortest_edge""": config.image_size + 32}      ,  )
    _a							:       int       					=			image_processor(images=prepare_img()      ,  return_tensors="""pt"""	)
    _a							:       int       					=			model(**UpperCAmelCase_	)
    _a							:       str       					=			outputs.logits

    if model_name.startswith("""deeplabv3_"""	):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            _a							:       Union[str, Any]       					=			torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]	)

        else:
            raise ValueError(F'''Unknown model name: {model_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3]      ,  UpperCAmelCase_      ,  atol=1e-4	)
    else:
        assert logits.shape == (1, 1_001)

        if model_name == "mobilenet_v2_1.4_224":
            _a							:       Union[str, Any]       					=			torch.tensor([0.0181, -1.0015, 0.4688]	)
        elif model_name == "mobilenet_v2_1.0_224":
            _a							:       List[Any]       					=			torch.tensor([0.2445, -1.1993, 0.1905]	)
        elif model_name == "mobilenet_v2_0.75_160":
            _a							:       Optional[int]       					=			torch.tensor([0.2482, 0.4136, 0.6669]	)
        elif model_name == "mobilenet_v2_0.35_96":
            _a							:       Dict       					=			torch.tensor([0.1451, -0.4624, 0.7192]	)
        else:
            _a							:       Union[str, Any]       					=			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							:       List[str]       					=			"""google/""" + model_name
        image_processor.push_to_hub(UpperCAmelCase_	)
        model.push_to_hub(UpperCAmelCase_	)


if __name__ == "__main__":
  a		:  Optional[Any]   			=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--model_name''',
      default='''mobilenet_v2_1.0_224''',
      type=str,
      help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'''
  )

  a		:  Any   			=			parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








'''simple docstring'''





import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


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

a		:  Optional[Any]   			=			[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]




class     UpperCamelCase					(  snake_case__					):
  a    :List[Any]      					=				RetriBertConfig
  a    :int      					=				None
  a    :List[Any]      					=				'retribert'



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    nn.Linear   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(__lowerCAmelCase		,    nn.Embedding   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
      elif isinstance(__lowerCAmelCase		,    nn.LayerNorm   ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0   )






a		:  Union[str, Any]   			=			R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''




@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'			,		snake_case__			,		)
class     UpperCamelCase					(  snake_case__					):



  def __init__( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      super().__init__(__lowerCAmelCase   )
      _a							:       Tuple       					=			config.projection_dim

      _a							:       int       					=			BertModel(__lowerCAmelCase   )
      _a							:       int       					=			None if config.share_encoders else BertModel(__lowerCAmelCase   )
      _a							:       Optional[int]       					=			nn.Dropout(config.hidden_dropout_prob   )
      _a							:       Dict       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )
      _a							:       Any       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )

      _a							:       Tuple       					=			nn.CrossEntropyLoss(reduction="""mean"""   )

      # Initialize weights and apply final processing
      self.post_init()



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase   )[1]
      else:
          # prepare implicit variables
          _a							:       Optional[int]       					=			input_ids.device
          _a							:       Optional[Any]       					=			input_ids.size()
          _a							:       str       					=			torch.zeros(__lowerCAmelCase		,    dtype=torch.long		,    device=__lowerCAmelCase   )
          _a							:       Optional[int]       					=			[None] * sent_encoder.config.num_hidden_layers
          _a							:       torch.Tensor       					=			sent_encoder.get_extended_attention_mask(
              __lowerCAmelCase		,    __lowerCAmelCase   )

          # define function for checkpointing
          def partial_encode(*__lowerCAmelCase   ):
              _a							:       Tuple       					=			sent_encoder.encoder(
                  inputs[0]		,    attention_mask=inputs[1]		,    head_mask=__lowerCAmelCase		,    )
              _a							:       List[str]       					=			encoder_outputs[0]
              _a							:       List[str]       					=			sent_encoder.pooler(__lowerCAmelCase   )
              return pooled_output

          # run embedding layer on everything at once
          _a							:       Optional[Any]       					=			sent_encoder.embeddings(
              input_ids=__lowerCAmelCase		,    position_ids=__lowerCAmelCase		,    token_type_ids=__lowerCAmelCase		,    inputs_embeds=__lowerCAmelCase   )
          # run encoding and pooling on one mini-batch at a time
          _a							:       Optional[int]       					=			[]
          for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
              _a							:       Union[str, Any]       					=			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Any       					=			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Optional[int]       					=			checkpoint.checkpoint(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
              pooled_output_list.append(__lowerCAmelCase   )
          return torch.cat(__lowerCAmelCase		,    dim=0   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       Any       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query		,    __lowerCAmelCase		,    )
      return self.project_query(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query if self.bert_doc is None else self.bert_doc		,    __lowerCAmelCase		,    )
      return self.project_doc(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    ):







      '''simple docstring'''


      _a							:       str       					=			input_ids_query.device
      _a							:       Tuple       					=			self.embed_questions(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       List[str]       					=			self.embed_answers(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       Dict       					=			torch.mm(__lowerCAmelCase		,    a_reps.t()   )
      _a							:       str       					=			self.ce_loss(__lowerCAmelCase		,    torch.arange(compare_scores.shape[1]   ).to(__lowerCAmelCase   )   )
      _a							:       Any       					=			self.ce_loss(compare_scores.t()		,    torch.arange(compare_scores.shape[0]   ).to(__lowerCAmelCase   )   )
      _a							:       List[str]       					=			(loss_qa + loss_aq) / 2
      return loss


