



'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def    lowerCamelCase__ (		_A       ):
  # vision encoder
  if "img_encoder.pos_embed" in name:
    a		:      List[str]       =  name.replace('img_encoder.pos_embed'				,    'vision_model.embeddings.position_embeddings'       )
  if "img_encoder.patch_embed.proj" in name:
    a		:      int       =  name.replace('img_encoder.patch_embed.proj'				,    'vision_model.embeddings.patch_embeddings.projection'       )
  if "img_encoder.patch_embed.norm" in name:
    a		:      List[Any]       =  name.replace('img_encoder.patch_embed.norm'				,    'vision_model.embeddings.layernorm'       )
  if "img_encoder.layers" in name:
    a		:      Union[str, Any]       =  name.replace('img_encoder.layers'				,    'vision_model.encoder.stages'       )
  if "blocks" in name and "res" not in name:
    a		:      Any       =  name.replace('blocks'				,    'layers'       )
  if "attn" in name and "pre_assign" not in name:
    a		:      Optional[int]       =  name.replace('attn'				,    'self_attn'       )
  if "proj" in name and "self_attn" in name and "text" not in name:
    a		:      int       =  name.replace('proj'				,    'out_proj'       )
  if "pre_assign_attn.attn.proj" in name:
    a		:      Union[str, Any]       =  name.replace('pre_assign_attn.attn.proj'				,    'pre_assign_attn.attn.out_proj'       )
  if "norm1" in name:
    a		:      Optional[Any]       =  name.replace('norm1'				,    'layer_norm1'       )
  if "norm2" in name and "pre_assign" not in name:
    a		:      Dict       =  name.replace('norm2'				,    'layer_norm2'       )
  if "img_encoder.norm" in name:
    a		:      List[Any]       =  name.replace('img_encoder.norm'				,    'vision_model.layernorm'       )
  # text encoder
  if "text_encoder.token_embedding" in name:
    a		:      str       =  name.replace('text_encoder.token_embedding'				,    'text_model.embeddings.token_embedding'       )
  if "text_encoder.positional_embedding" in name:
    a		:      str       =  name.replace('text_encoder.positional_embedding'				,    'text_model.embeddings.position_embedding.weight'       )
  if "text_encoder.transformer.resblocks." in name:
    a		:      int       =  name.replace('text_encoder.transformer.resblocks.'				,    'text_model.encoder.layers.'       )
  if "ln_1" in name:
    a		:      Union[str, Any]       =  name.replace('ln_1'				,    'layer_norm1'       )
  if "ln_2" in name:
    a		:      Optional[int]       =  name.replace('ln_2'				,    'layer_norm2'       )
  if "c_fc" in name:
    a		:      List[str]       =  name.replace('c_fc'				,    'fc1'       )
  if "c_proj" in name:
    a		:      Union[str, Any]       =  name.replace('c_proj'				,    'fc2'       )
  if "text_encoder" in name:
    a		:      Any       =  name.replace('text_encoder'				,    'text_model'       )
  if "ln_final" in name:
    a		:      Union[str, Any]       =  name.replace('ln_final'				,    'final_layer_norm'       )
  # projection layers
  if "img_projector.linear_hidden." in name:
    a		:      Optional[Any]       =  name.replace('img_projector.linear_hidden.'				,    'visual_projection.'       )
  if "img_projector.linear_out." in name:
    a		:      Optional[int]       =  name.replace('img_projector.linear_out.'				,    'visual_projection.3.'       )
  if "text_projector.linear_hidden" in name:
    a		:      int       =  name.replace('text_projector.linear_hidden'				,    'text_projection'       )
  if "text_projector.linear_out" in name:
    a		:      int       =  name.replace('text_projector.linear_out'				,    'text_projection.3'       )

  return name
def    lowerCamelCase__ (		_A				,    _A       ):
  for key in orig_state_dict.copy().keys():
    a		:      str       =  orig_state_dict.pop(_A       )

    if "qkv" in key:
      # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      a		:      List[Any]       =  key.split('.'       )
      a     ,			a		:      int       =  int(key_split[2]       ), int(key_split[4]       )
      a		:      Dict       =  config.vision_config.hidden_size
      if "weight" in key:
        a		:      Dict       =  val[:dim, :]
        a		:      Optional[Any]       =  val[dim : dim * 2, :]
        a		:      Dict       =  val[-dim:, :]
      else:
        a		:      Optional[Any]       =  val[:dim]
        a		:      Optional[int]       =  val[dim : dim * 2]
        a		:      Optional[int]       =  val[-dim:]
    elif "in_proj" in key:
      # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      a		:      List[Any]       =  key.split('.'       )
      a		:      List[str]       =  int(key_split[3]       )
      a		:      Union[str, Any]       =  config.text_config.hidden_size
      if "weight" in key:
        a		:      str       =  val[:dim, :]
        a		:      Any       =  val[
            dim : dim * 2, :
        ]
        a		:      Dict       =  val[-dim:, :]
      else:
        a		:      Dict       =  val[:dim]
        a		:      int       =  val[dim : dim * 2]
        a		:      Union[str, Any]       =  val[-dim:]
    else:
      a		:      int       =  rename_key(_A       )
      # squeeze if necessary
      if (
          "text_projection.0" in new_name
          or "text_projection.3" in new_name
          or "visual_projection.0" in new_name
          or "visual_projection.3" in new_name
      ):
        a		:      List[str]       =  val.squeeze_()
      else:
        a		:      int       =  val

  return orig_state_dict
def    lowerCamelCase__ (		):
  a		:      int       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  a		:      str       =  Image.open(requests.get(_A				,    stream=_A       ).raw       )
  return im
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A="groupvit-gcc-yfcc"				,    _A=False       ):
  a		:      List[Any]       =  GroupViTConfig()
  a		:      List[str]       =  GroupViTModel(_A       ).eval()

  a		:      Optional[int]       =  torch.load(_A				,    map_location='cpu'       )['model']
  a		:      Optional[Any]       =  convert_state_dict(_A				,    _A       )
  a     ,			a		:      Optional[Any]       =  model.load_state_dict(_A				,    strict=_A       )
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_A       ) == 0)

  # verify result
  a		:      List[str]       =  CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'       )
  a		:      str       =  prepare_img()
  a		:      str       =  processor(text=['a photo of a cat', 'a photo of a dog']				,    images=_A				,    padding=_A				,    return_tensors='pt'       )

  with torch.no_grad():
    a		:      str       =  model(**_A       )

  if model_name == "groupvit-gcc-yfcc":
    a		:      str       =  torch.tensor([[13.3523, 6.3629]]       )
  elif model_name == "groupvit-gcc-redcaps":
    a		:      str       =  torch.tensor([[16.1873, 8.6230]]       )
  else:
    raise ValueError(f"""Model name {model_name} not supported."""       )
  assert torch.allclose(outputs.logits_per_image				,    _A				,    atol=1E-3       )

  processor.save_pretrained(_A       )
  model.save_pretrained(_A       )
  print('Successfully saved processor and model to'				,    _A       )

  if push_to_hub:
    print('Pushing to the hub...'       )
    processor.push_to_hub(_A				,    organization='nielsr'       )
    model.push_to_hub(_A				,    organization='nielsr'       )


if __name__ == "__main__":
     lowerCAmelCase:      List[Any]		 =		argparse.ArgumentParser()
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
     )
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
     parser.add_argument(
         '--model_name',
         default='groupvit-gccy-fcc',
         type=str,
         help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
     )
     lowerCAmelCase:      Optional[Any]		 =		parser.parse_args()

     convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.bert.modeling_flax_bert import (
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForNextSentencePrediction,
         FlaxBertForPreTraining,
         FlaxBertForQuestionAnswering,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertModel,
     )

class 							a__(      unittest.TestCase			):







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Any=13		,    __snake_case				:						Any=7		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						Optional[Any]=99		,    __snake_case				:						List[str]=32		,    __snake_case				:						List[Any]=5		,    __snake_case				:						int=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						int="gelu"		,    __snake_case				:						Tuple=0.1		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						Any=5_12		,    __snake_case				:						Tuple=16		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						str=0.02		,    __snake_case				:						Union[str, Any]=4		,    ):
      a		:      List[Any]       =  parent
      a		:      List[str]       =  batch_size
      a		:      Dict       =  seq_length
      a		:      str       =  is_training
      a		:      Optional[int]       =  use_attention_mask
      a		:      Union[str, Any]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Dict       =  vocab_size
      a		:      Tuple       =  hidden_size
      a		:      Optional[Any]       =  num_hidden_layers
      a		:      List[str]       =  num_attention_heads
      a		:      Tuple       =  intermediate_size
      a		:      Dict       =  hidden_act
      a		:      List[str]       =  hidden_dropout_prob
      a		:      str       =  attention_probs_dropout_prob
      a		:      str       =  max_position_embeddings
      a		:      Tuple       =  type_vocab_size
      a		:      Optional[Any]       =  type_sequence_label_size
      a		:      Union[str, Any]       =  initializer_range
      a		:      Union[str, Any]       =  num_choices







    def     lowercase_ (   self				:						Any			):
      a		:      List[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[str]       =  None
      if self.use_attention_mask:
        a		:      List[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Dict       =  None
      if self.use_token_type_ids:
        a		:      List[str]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size			)

      a		:      Dict       =  BertConfig(
          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		,    is_decoder=__snake_case		,    initializer_range=self.initializer_range		,    )

      return config, input_ids, token_type_ids, attention_mask







    def     lowercase_ (   self				:						Tuple			):
      a		:      Optional[int]       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Union[str, Any]       =  config_and_inputs
      a		:      str       =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						List[str]			):
      a		:      int       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Tuple       =  config_and_inputs

      a		:      int       =  True
      a		:      int       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      a		:      Optional[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2			)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )

@require_flax
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					True

    lowercase__				=					(
        (
            FlaxBertModel,
            FlaxBertForPreTraining,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForQuestionAnswering,
            FlaxBertForNextSentencePrediction,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      str       =  FlaxBertModelTester(self			)







    @slow
    def     lowercase_ (   self				:						Any			):
      # Only check this for base model, not necessary for all model classes.
      # This will also help speed-up tests.
      a		:      Optional[Any]       =  FlaxBertModel.from_pretrained('bert-base-cased'			)
      a		:      Union[str, Any]       =  model(np.ones((1, 1)			)			)
      self.assertIsNotNone(__snake_case			)