
'''simple docstring'''






import inspect
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import DecisionTransformerModel
 from transformers.models.decision_transformer.modeling_decision_transformer import (
     DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )

class  UpperCAmelCase__						:



    def __init__(	self      :       Union[str, Any],__A      :       List[str],__A      :       Union[str, Any]=1_3,__A      :       str=7,__A      :       List[Any]=6,__A      :       Optional[Any]=1_7,__A      :       Tuple=2_3,__A      :       int=1_1,__A      :       Optional[int]=True,):
          _lowerCamelCase				:   Optional[int]								=   parent
          _lowerCamelCase				:   Dict								=   batch_size
          _lowerCamelCase				:   Dict								=   seq_length
          _lowerCamelCase				:   List[Any]								=   act_dim
          _lowerCamelCase				:   Dict								=   state_dim
          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   Optional[int]								=   max_length
          _lowerCamelCase				:   int								=   is_training



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Optional[int]								=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
          _lowerCamelCase				:   Any								=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
          _lowerCamelCase				:   Any								=   floats_tensor((self.batch_size, self.seq_length, 1)     )
          _lowerCamelCase				:   List[Any]								=   floats_tensor((self.batch_size, self.seq_length, 1)     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor((self.batch_size, self.seq_length),vocab_size=1_0_0_0     )
          _lowerCamelCase				:   Dict								=   random_attention_mask((self.batch_size, self.seq_length)     )

          _lowerCamelCase				:   Optional[Any]								=   self.get_config()

          return (
              config,
              states,
              actions,
              rewards,
              returns_to_go,
              timesteps,
              attention_mask,
          )



    def   lowerCamelCase_  (	self      :       int     ):
          return DecisionTransformerConfig(
              batch_size=self.batch_size,seq_length=self.seq_length,act_dim=self.act_dim,state_dim=self.state_dim,hidden_size=self.hidden_size,max_length=self.max_length,)



    def   lowerCamelCase_  (	self      :       int,__A      :       List[str],__A      :       Union[str, Any],__A      :       Dict,__A      :       Any,__A      :       List[str],__A      :       Any,__A      :       str,):
          _lowerCamelCase				:   Optional[int]								=   DecisionTransformerModel(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Any								=   model(__A,__A,__A,__A,__A,__A     )

          self.parent.assertEqual(result.state_preds.shape,states.shape     )
          self.parent.assertEqual(result.action_preds.shape,actions.shape     )
          self.parent.assertEqual(result.return_preds.shape,returns_to_go.shape     )
          self.parent.assertEqual(
              result.last_hidden_state.shape,(self.batch_size, self.seq_length * 3, self.hidden_size)     )  # seq length *3 as there are 3 modelities: states, returns and actions



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   self.prepare_config_and_inputs()
          (
              (
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		
          )				:   Optional[int]								=   config_and_inputs
          _lowerCamelCase				:   List[str]								=   {
              "states": states,
              "actions": actions,
              "rewards": rewards,
              "returns_to_go": returns_to_go,
              "timesteps": timesteps,
              "attention_mask": attention_mask,
          }
          return config, inputs_dict


@require_torch
class  UpperCAmelCase__						(       A						,	A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(DecisionTransformerModel,) if is_torch_available() else ()
    lowerCAmelCase_				    =		()
    lowerCAmelCase_				    =		{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

    # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
    lowerCAmelCase_				    =		False

    # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Union[str, Any]								=   DecisionTransformerModelTester(self     )
          _lowerCamelCase				:   List[Any]								=   ConfigTester(self,config_class=__A,hidden_size=3_7     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          self.config_tester.run_common_tests()



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A     )



    @slow
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase				:   Optional[int]								=   DecisionTransformerModel.from_pretrained(__A     )
                self.assertIsNotNone(__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class(__A     )
                _lowerCamelCase				:   Dict								=   inspect.signature(model.forward     )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _lowerCamelCase				:   Tuple								=   [*signature.parameters.keys()]

                _lowerCamelCase				:   List[str]								=   [
                    "states",
                    "actions",
                    "rewards",
                    "returns_to_go",
                    "timesteps",
                    "attention_mask",
                ]

                self.assertListEqual(arg_names[: len(__A     )],__A     )


@require_torch
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   str								=   2  # number of steps of autoregressive prediction we will perform
          _lowerCamelCase				:   Tuple								=   1_0  # defined by the RL environment, may be normalized
          _lowerCamelCase				:   List[str]								=   DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"     )
          _lowerCamelCase				:   str								=   model.to(__A     )
          _lowerCamelCase				:   Optional[Any]								=   model.config
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[int]								=   torch.randn(1,1,config.state_dim     ).to(device=__A,dtype=torch.floataa     )  # env.reset()

          _lowerCamelCase				:   List[Any]								=   torch.tensor(
              [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]],device=__A     )

          _lowerCamelCase				:   Dict								=   torch.tensor(__A,device=__A,dtype=torch.floataa     ).reshape(1,1,1     )
          _lowerCamelCase				:   int								=   state
          _lowerCamelCase				:   int								=   torch.zeros(1,0,config.act_dim,device=__A,dtype=torch.floataa     )
          _lowerCamelCase				:   List[Any]								=   torch.zeros(1,0,device=__A,dtype=torch.floataa     )
          _lowerCamelCase				:   int								=   torch.tensor(0,device=__A,dtype=torch.long     ).reshape(1,1     )

          for step in range(__A     ):
                _lowerCamelCase				:   Any								=   torch.cat([actions, torch.zeros(1,1,config.act_dim,device=__A     )],dim=1     )
                _lowerCamelCase				:   Any								=   torch.cat([rewards, torch.zeros(1,1,device=__A     )],dim=1     )

                _lowerCamelCase				:   Optional[int]								=   torch.ones(1,states.shape[1]     ).to(dtype=torch.long,device=states.device     )

                with torch.no_grad():
                      _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   model(
                          states=__A,actions=__A,rewards=__A,returns_to_go=__A,timesteps=__A,attention_mask=__A,return_dict=__A,)

                self.assertEqual(action_pred.shape,actions.shape     )
                self.assertTrue(torch.allclose(action_pred[0, -1],expected_outputs[step],atol=1e-4     )     )
                _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Any								=   (  # env.step(action)
                    torch.randn(1,1,config.state_dim     ).to(device=__A,dtype=torch.floataa     ),
                    1.0,
                    False,
                    {},
                )

                _lowerCamelCase				:   int								=   action_pred[0, -1]
                _lowerCamelCase				:   List[Any]								=   torch.cat([states, state],dim=1     )
                _lowerCamelCase				:   Dict								=   returns_to_go[0, -1] - reward
                _lowerCamelCase				:   Any								=   torch.cat([returns_to_go, pred_return.reshape(1,1,1     )],dim=1     )
                _lowerCamelCase				:   Dict								=   torch.cat(
                    [timesteps, torch.ones((1, 1),device=__A,dtype=torch.long     ) * (step + 1)],dim=1     )

'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




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

      return name







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase				:   Optional[int]								=   orig_state_dict.pop(_lowerCAmelCase							)

            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
                  _lowerCamelCase				:   Any								=   key.split("."							)
                  _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   int(key_split[2]							), int(key_split[4]							)
                  _lowerCamelCase				:   List[Any]								=   config.vision_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   int								=   val[dim : dim * 2, :]
                        _lowerCamelCase				:   str								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   Optional[int]								=   val[:dim]
                        _lowerCamelCase				:   str								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Tuple								=   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
                  _lowerCamelCase				:   Optional[Any]								=   key.split("."							)
                  _lowerCamelCase				:   int								=   int(key_split[3]							)
                  _lowerCamelCase				:   Tuple								=   config.text_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   str								=   val[
                            dim : dim * 2, :
                        ]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   str								=   val[:dim]
                        _lowerCamelCase				:   Optional[Any]								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:]
            else:
                  _lowerCamelCase				:   int								=   rename_key(_lowerCAmelCase							)
                  # 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
                  ):
                        _lowerCamelCase				:   Any								=   val.squeeze_()
                  else:
                        _lowerCamelCase				:   Union[str, Any]								=   val

      return orig_state_dict







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase				:   Any								=   Image.open(requests.get(_lowerCAmelCase				,	stream=_lowerCAmelCase							).raw							)
      return im







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    str="groupvit-gcc-yfcc"				,	_lowerCAmelCase   :    List[str]=False							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   GroupViTConfig()
      _lowerCamelCase				:   Union[str, Any]								=   GroupViTModel(_lowerCAmelCase							).eval()

      _lowerCamelCase				:   Any								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]
      _lowerCamelCase				:   Union[str, Any]								=   convert_state_dict(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   model.load_state_dict(_lowerCAmelCase				,	strict=_lowerCAmelCase							)
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase							) == 0)

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

      with torch.no_grad():
            _lowerCamelCase				:   List[str]								=   model(**_lowerCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            _lowerCamelCase				:   List[Any]								=   torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            _lowerCamelCase				:   Optional[Any]								=   torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]							)
      else:
            raise ValueError(F'Model name {model_name} not supported.'							)
      assert torch.allclose(outputs.logits_per_image				,	_lowerCAmelCase				,	atol=1E-3							)

      processor.save_pretrained(_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)
      print("Successfully saved processor and model to"				,	_lowerCAmelCase							)

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


if __name__ == "__main__":
 UpperCAmelCase_       :   Optional[int]   =       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`.',
 )
 UpperCAmelCase_       :   Union[str, Any]   =       parser.parse_args()

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