






"""simple docstring"""







import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
  from transformers.models.dpr.modeling_dpr import (
      DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )







class 					lowerCamelCase__					:






    def __init__( self	,A	,A=13	,A=7	,A=False	,A=True	,A=True	,A=True	,A=99	,A=32	,A=5	,A=4	,A=37	,A="gelu"	,A=0.1	,A=0.1	,A=512	,A=16	,A=2	,A=0.02	,A=3	,A=4	,A=None	,A=0	,):
          UpperCAmelCase							=       parent
          UpperCAmelCase							=       batch_size
          UpperCAmelCase							=       seq_length
          UpperCAmelCase							=       is_training
          UpperCAmelCase							=       use_input_mask
          UpperCAmelCase							=       use_token_type_ids
          UpperCAmelCase							=       use_labels
          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       hidden_size
          UpperCAmelCase							=       num_hidden_layers
          UpperCAmelCase							=       num_attention_heads
          UpperCAmelCase							=       intermediate_size
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       hidden_dropout_prob
          UpperCAmelCase							=       attention_probs_dropout_prob
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       type_vocab_size
          UpperCAmelCase							=       type_sequence_label_size
          UpperCAmelCase							=       initializer_range
          UpperCAmelCase							=       num_labels
          UpperCAmelCase							=       num_choices
          UpperCAmelCase							=       scope
          UpperCAmelCase							=       projection_dim






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size					)

          UpperCAmelCase							=       None
          if self.use_input_mask:
                UpperCAmelCase							=       random_attention_mask([self.batch_size, self.seq_length]					)

          UpperCAmelCase							=       None
          if self.use_token_type_ids:
                UpperCAmelCase							=       ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size					)

          UpperCAmelCase							=       None
          UpperCAmelCase							=       None
          UpperCAmelCase							=       None
          if self.use_labels:
                UpperCAmelCase							=       ids_tensor([self.batch_size]	,self.type_sequence_label_size					)
                UpperCAmelCase							=       ids_tensor([self.batch_size, self.seq_length]	,self.num_labels					)
                UpperCAmelCase							=       ids_tensor([self.batch_size]	,self.num_choices					)

          UpperCAmelCase							=       self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def 				_UpperCamelCase							( self					):
          return DPRConfig(
              projection_dim=self.projection_dim	,vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,max_position_embeddings=self.max_position_embeddings	,type_vocab_size=self.type_vocab_size	,initializer_range=self.initializer_range	,)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A					):
          UpperCAmelCase							=       DPRContextEncoder(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(A	,attention_mask=A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A					)
          self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A					):
          UpperCAmelCase							=       DPRQuestionEncoder(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(A	,attention_mask=A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A					)
          self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A					):
          UpperCAmelCase							=       DPRReader(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(
              A	,attention_mask=A	,)

          self.parent.assertEqual(result.start_logits.shape	,(self.batch_size, self.seq_length)					)
          self.parent.assertEqual(result.end_logits.shape	,(self.batch_size, self.seq_length)					)
          self.parent.assertEqual(result.relevance_logits.shape	,(self.batch_size,)					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					
          )							=       config_and_inputs
          UpperCAmelCase							=       {"""input_ids""": input_ids}
          return config, inputs_dict







@require_torch
class 					lowerCamelCase__					(       snake_case    ,     snake_case    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			(
        (
            DPRContextEncoder,
            DPRQuestionEncoder,
            DPRReader,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		=			{'''feature-extraction''': DPRQuestionEncoder} if is_torch_available() else {}

    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False  # why?
    SCREAMING_SNAKE_CASE		=			False
    SCREAMING_SNAKE_CASE		=			False






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       DPRModelTester(self					)
          UpperCAmelCase							=       ConfigTester(self	,config_class=A	,hidden_size=37					)






    def 				_UpperCamelCase							( self					):
          self.config_tester.run_common_tests()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_context_encoder(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_question_encoder(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_reader(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()[0]

          UpperCAmelCase							=       DPRQuestionEncoder(config=A					)
          model.to(A					)
          model.eval()

          with tempfile.TemporaryDirectory() as tmp_dirname:
                model.save_pretrained(A					)
                UpperCAmelCase							=       DPRQuestionEncoder.from_pretrained(A	,projection_dim=512					)

          self.assertIsNotNone(A					)






    @slow
    def 				_UpperCamelCase							( self					):
          for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase							=       DPRContextEncoder.from_pretrained(A					)
                self.assertIsNotNone(A					)

          for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase							=       DPRContextEncoder.from_pretrained(A					)
                self.assertIsNotNone(A					)

          for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase							=       DPRQuestionEncoder.from_pretrained(A					)
                self.assertIsNotNone(A					)

          for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase							=       DPRReader.from_pretrained(A					)
                self.assertIsNotNone(A					)







@require_torch
class 					lowerCamelCase__					(       unittest.TestCase ):






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       DPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""	,return_dict=A					)
          model.to(A					)

          UpperCAmelCase							=       torch.tensor(
              [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]	,dtype=torch.long	,device=A					)  # [CLS] hello, is my dog cute? [SEP]
          UpperCAmelCase							=       model(A					)[0]  # embedding shape = (1, 768)
          # compare the actual values for a slice.
          UpperCAmelCase							=       torch.tensor(
              [
                  [
                      0.03236253,
                      0.12753335,
                      0.16818509,
                      0.00279786,
                      0.3896933,
                      0.24264945,
                      0.2178971,
                      -0.02335227,
                      -0.08481959,
                      -0.14324117,
                  ]
              ]	,dtype=torch.float	,device=A	,)
          self.assertTrue(torch.allclose(output[:, :10]	,A	,atol=1e-4					)					)






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       DPRReaderTokenizer.from_pretrained("""facebook/dpr-reader-single-nq-base"""					)
          UpperCAmelCase							=       DPRReader.from_pretrained("""facebook/dpr-reader-single-nq-base"""					)
          model.to(A					)

          UpperCAmelCase							=       tokenizer(
              questions="""What is love ?"""	,titles="""Haddaway"""	,texts="""What Is Love is a song recorded by the artist Haddaway"""	,padding=A	,return_tensors="""pt"""	,)
          encoded_inputs.to(A					)

          UpperCAmelCase							=       model(**A					)

          # compare the actual values for a slice.
          UpperCAmelCase							=       torch.tensor(
              [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]]	,dtype=torch.float	,device=A	,)

          UpperCAmelCase							=       torch.tensor(
              [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]]	,dtype=torch.float	,device=A	,)
          self.assertTrue(torch.allclose(outputs.start_logits[:, :10]	,A	,atol=1e-4					)					)
          self.assertTrue(torch.allclose(outputs.end_logits[:, :10]	,A	,atol=1e-4					)					)











"""simple docstring"""







import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase     			=  logging.get_logger()







@dataclass
class 					lowerCamelCase__					:
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )
    SCREAMING_SNAKE_CASE		=			field(default_factory=snake_case )






    def 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          UpperCAmelCase							=       len(list(m.modules()					)					) == 1 or isinstance(A	,nn.Convad					) or isinstance(A	,nn.BatchNormad					)
          if has_not_submodules:
                self.traced.append(A					)
                UpperCAmelCase							=       m






    def __call__( self	,A					):
          for name, m in self.module.named_modules():
                self.handles.append(m.register_forward_hook(partial(self._forward_hook	,name=A					)					)					)
          self.module(A					)
          [x.remove() for x in self.handles]
          return self






    @property
    def 				_UpperCamelCase							( self					):
          # check the len of the state_dict keys to see if we have learnable params
          return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()					)					) > 0}







class 					lowerCamelCase__					(       nn.Module ):






    def __init__( self	,A					):
          super().__init__()

          UpperCAmelCase							=       []
          # - get the stem
          feature_blocks.append(("""conv1""", model.stem)					)
          # - get all the feature blocks
          for k, v in model.trunk_output.named_children():
                assert k.startswith("""block"""					), F'''Unexpected layer name {k}'''
                UpperCAmelCase							=       len(A					) + 1
                feature_blocks.append((F'''res{block_index}''', v)					)

          UpperCAmelCase							=       nn.ModuleDict(A					)






    def 				_UpperCamelCase							( self	,A					):
          return get_trunk_forward_outputs(
              A	,out_feat_keys=A	,feature_blocks=self._feature_blocks	,)







class 					lowerCamelCase__					(       snake_case ):






    def 				_UpperCamelCase							( self					):
          return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def _a (		_snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       RegNetConfig(depths=[2, 7, 17, 1]      ,       hidden_sizes=[8, 8, 8, 8]      ,       groups_width=8							)
      if "in1k" in model_name:
            UpperCAmelCase							=       RegNetForImageClassification(_snake_case							)
      else:
            UpperCAmelCase							=       RegNetModel(_snake_case							)
      # create from model (with small weights)
      UpperCAmelCase							=       FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27      ,       group_width=1010      ,       w_a=1744      ,       w_a=620.83      ,       w_m=2.52							)							)							)

      with torch.no_grad():
            UpperCAmelCase							=       from_model.eval()
            UpperCAmelCase							=       our_model.eval()

            UpperCAmelCase							=       torch.randn((1, 3, 32, 32)							)
            # trace both
            UpperCAmelCase							=       Tracker(_snake_case							)
            UpperCAmelCase							=       dest_tracker(_snake_case							).parametrized

            pprint(dest_tracker.nameamodule							)
            UpperCAmelCase							=       Tracker(_snake_case							)
            UpperCAmelCase							=       src_tracker(_snake_case							).parametrized

      # convert the keys -> module dict to keys -> params
      def to_params_dict(_snake_case							):
            UpperCAmelCase							=       OrderedDict()
            for name, module in dict_with_modules.items():
                  for param_name, param in module.state_dict().items():
                        UpperCAmelCase							=       param
            return params_dict

      UpperCAmelCase							=       {}

      UpperCAmelCase							=       to_params_dict(_snake_case							)
      UpperCAmelCase							=       to_params_dict(_snake_case							)

      for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()      ,       dst_state_dict.items()							):
            UpperCAmelCase							=       dest_key
            logger.info(F'''{src_key} -> {dest_key}'''							)
      # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
      if "in1k" in model_name:
            UpperCAmelCase							=       """classifier.1.weight"""
            UpperCAmelCase							=       """classifier.1.bias"""

      return from_to_ours_keys

def _a (		_snake_case      ,       _snake_case = None      ,       _snake_case = True							):







      """simple docstring"""






      UpperCAmelCase							=       """imagenet-1k-id2label.json"""
      UpperCAmelCase							=       1000

      UpperCAmelCase							=       """huggingface/label-files"""
      UpperCAmelCase							=       num_labels
      UpperCAmelCase							=       json.load(open(cached_download(hf_hub_url(_snake_case      ,       _snake_case      ,       repo_type="""dataset"""							)							)      ,       """r"""							)							)
      UpperCAmelCase							=       {int(_snake_case							): v for k, v in idalabel.items()}

      UpperCAmelCase							=       idalabel
      UpperCAmelCase							=       {v: k for k, v in idalabel.items()}

      UpperCAmelCase							=       partial(_snake_case      ,       num_labels=_snake_case      ,       idalabel=_snake_case      ,       labelaid=_snake_case							)

      UpperCAmelCase							=       {
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
          # finetuned on imagenet
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]      ,       groups_width=1010							),
      }

      # add seer weights logic
      def load_using_classy_vision(_snake_case							) -> Tuple[Dict, Dict]:
            UpperCAmelCase							=       torch.hub.load_state_dict_from_url(_snake_case      ,       model_dir=str(_snake_case							)      ,       map_location="""cpu"""							)
            # check if we have a head, if yes add it
            UpperCAmelCase							=       files["""classy_state_dict"""]["""base_model"""]["""model"""]
            return model_state_dict["trunk"], model_state_dict["heads"]

      UpperCAmelCase							=       {
          """regnet-y-10b-seer""": partial(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,       ),
          """regnet-y-10b-seer-in1k""": partial(
              _snake_case      ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,       ),
      }

      UpperCAmelCase							=       get_from_to_our_keys(_snake_case							)

      if not (save_directory / F'''{model_name}.pth''').exists():
            logger.info("""Loading original state_dict."""							)
            UpperCAmelCase			,					UpperCAmelCase							=       names_to_from_model[model_name]()
            UpperCAmelCase							=       from_state_dict_trunk
            if "in1k" in model_name:
                  # add the head
                  UpperCAmelCase							=       {**from_state_dict_trunk, **from_state_dict_head}
            logger.info("""Done!"""							)

            UpperCAmelCase							=       {}

            UpperCAmelCase							=       list(from_state_dict.keys()							)
            UpperCAmelCase							=       R"""\.block.-part."""
            # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
            for key in from_state_dict.keys():
                  # remove the weird "block[0,1]-part" from the key
                  UpperCAmelCase							=       re.sub(_snake_case      ,       """"""      ,       _snake_case							)
                  # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
                  UpperCAmelCase							=       from_to_ours_keys[src_key]
                  # store the parameter with our key
                  UpperCAmelCase							=       from_state_dict[key]
                  not_used_keys.remove(_snake_case							)
            # check that all keys have been updated
            assert len(_snake_case							) == 0, F'''Some keys where not used {','.join(_snake_case							)}'''

            logger.info(F'''The following keys were not used: {','.join(_snake_case							)}'''							)

            # save our state dict to disk
            torch.save(_snake_case      ,       save_directory / F'''{model_name}.pth'''							)

            del converted_state_dict
      else:
            logger.info("""The state_dict was already stored on disk."""							)
      if push_to_hub:
            logger.info(F'''Token is {os.environ['HF_TOKEN']}'''							)
            logger.info("""Loading our model."""							)
            # create our model
            UpperCAmelCase							=       names_to_config[model_name]
            UpperCAmelCase							=       RegNetModel
            if "in1k" in model_name:
                  UpperCAmelCase							=       RegNetForImageClassification
            UpperCAmelCase							=       our_model_func(_snake_case							)
            # place our model to the meta device (so remove all the weights)
            our_model.to(torch.device("""meta"""							)							)
            logger.info("""Loading state_dict in our model."""							)
            # load state dict
            UpperCAmelCase							=       our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                _snake_case      ,       _snake_case      ,       [save_directory / F'''{model_name}.pth''']							)
            logger.info("""Finally, pushing!"""							)
            # push it to hub
            our_model.push_to_hub(
                repo_path_or_name=save_directory / model_name      ,       commit_message="""Add model"""      ,       output_dir=save_directory / model_name      ,       )
            UpperCAmelCase							=       384
            # we can use the convnext one
            UpperCAmelCase							=       AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      ,       size=_snake_case							)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / model_name      ,       commit_message="""Add image processor"""      ,       output_dir=save_directory / model_name      ,       )


if __name__ == "__main__":
  _UpperCamelCase     			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
          """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  _UpperCamelCase     			=  parser.parse_args()

  _UpperCamelCase     			=  args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



