



"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__a           =							logging.get_logger(__name__)
@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     List[nn.Module]		=	field(default_factory=_lowerCAmelCase							)
					_A						:     list		=	field(default_factory=_lowerCAmelCase							)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Union[str, Any]		,	snake_case:				Tensor		,	snake_case:				Tensor		)  ->      List[str]:
											snake_case_					:Optional[int]		=     len(list(m.modules()		)		) == 1 or isinstance(snake_case		,	nn.Convad		) or isinstance(snake_case		,	nn.BatchNormad		)
											if has_not_submodules:
																	if not isinstance(snake_case		,	snake_case		):
																							self.traced.append(snake_case		)






					def __call__(			self:				Optional[Any]		,	snake_case:				Tensor		)  ->      Optional[Any]:
											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook		)		)
											self.module(snake_case		)
											[x.remove() for x in self.handles]
											return self






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case		: len(list(x.state_dict().keys()		)		) > 0		,	self.traced		)		)

@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     nn.Module
					_A						:     int		=	0
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)






					def __call__(			self:				List[Any]		,	snake_case:				Tensor		)  ->      List[Any]:
											snake_case_					:Optional[int]		=     Tracker(self.dest		)(snake_case		).parametrized
											snake_case_					:int		=     Tracker(self.src		)(snake_case		).parametrized

											snake_case_					:Optional[Any]		=     list(filter(lambda snake_case		: type(snake_case		) not in self.src_skip		,	snake_case		)		)
											snake_case_					:Any		=     list(filter(lambda snake_case		: type(snake_case		) not in self.dest_skip		,	snake_case		)		)

											if len(snake_case		) != len(snake_case		):
																	raise Exception(
																	    f"""Numbers of operations are different. Source module has {len(snake_case		)} operations while"""
																	    f""" destination module has {len(snake_case		)}."""		)

											for dest_m, src_m in zip(snake_case		,	snake_case		):
																	dest_m.load_state_dict(src_m.state_dict()		)
																	if self.verbose == 1:
																							print(f"""Transfered from={src_m} to={dest_m}"""		)

def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     from_model.state_dict()
						snake_case_					:Optional[Any]		=     our_model.state_dict()
						snake_case_					:Dict		=     our_model.config
						snake_case_					:List[str]		=     []
						for stage_idx in range(len(config.hidden_sizes      )      ):
												for block_id in range(config.depths[stage_idx]      ):
																		snake_case_					:List[Any]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		snake_case_					:Dict		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)      )
																		snake_case_					:Optional[int]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		snake_case_					:Tuple		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

																		all_keys.append((from_key, to_key)      )

						for from_key, to_key in all_keys:
												snake_case_					:List[str]		=     from_state_dict.pop(_lowercase      )

						our_model.load_state_dict(_lowercase      )
						return our_model
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase = True,   ):







						'''simple docstring'''


						print(f"""Downloading weights for {name}..."""      )
						snake_case_					:Union[str, Any]		=     cached_download(_lowercase      )
						print(f"""Converting {name}..."""      )
						snake_case_					:Union[str, Any]		=     torch.load(_lowercase      )["""state_dict"""]
						from_model.load_state_dict(_lowercase      )
						from_model.eval()
						with torch.no_grad():
												snake_case_					:List[str]		=     VanForImageClassification(_lowercase      ).eval()
												snake_case_					:Tuple		=     ModuleTransfer(src=_lowercase,   dest=_lowercase      )
												snake_case_					:int		=     torch.randn((1, 3, 224, 224)      )
												module_transfer(_lowercase      )
												snake_case_					:int		=     copy_parameters(_lowercase,   _lowercase      )

						if not torch.allclose(from_model(_lowercase      ),   our_model(_lowercase      ).logits      ):
												raise ValueError("""The model logits don't match the original one."""      )

						snake_case_					:List[str]		=     name
						print(_lowercase      )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,   commit_message="""Add model""",   use_temp_dir=_lowercase,   )

												# we can use the convnext one
												snake_case_					:str		=     AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name,   commit_message="""Add image processor""",   use_temp_dir=_lowercase,   )

												print(f"""Pushed {checkpoint_name}"""      )
def   A_    (     _lowercase,   _lowercase = None,   _lowercase = True      ):







						'''simple docstring'''


						snake_case_					:str		=     """imagenet-1k-id2label.json"""
						snake_case_					:int		=     1000

						snake_case_					:List[Any]		=     """huggingface/label-files"""
						snake_case_					:List[Any]		=     num_labels
						snake_case_					:Optional[Any]		=     json.load(open(hf_hub_download(_lowercase,   _lowercase,   repo_type="""dataset"""      ),   """r"""      )      )
						snake_case_					:int		=     {int(_lowercase      ): v for k, v in idalabel.items()}

						snake_case_					:str		=     idalabel
						snake_case_					:List[str]		=     {v: k for k, v in idalabel.items()}

						snake_case_					:str		=     partial(_lowercase,   num_labels=_lowercase,   idalabel=_lowercase,   labelaid=_lowercase      )

						snake_case_					:Dict		=     {
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256],   depths=[3, 3, 5, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[2, 2, 4, 2],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 3, 12, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512],   depths=[3, 5, 27, 3],   mlp_ratios=[8, 8, 4, 4],   ),
						}

						snake_case_					:Optional[int]		=     {
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						snake_case_					:str		=     {
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    _lowercase,   names_to_config[model_name],   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _lowercase,   _lowercase,   checkpoint=names_to_original_checkpoints[model_name],   from_model=names_to_original_models[model_name](),   save_directory=_lowercase,   push_to_hub=_lowercase,   )


if __name__ == "__main__":
						__a           =							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 resnet* architecture,"
						        " currently: van-tiny/small/base/large. 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(
						    "--van_dir",
						    required=True,
						    type=Path,
						    help=(
						        "A path to VAN's original implementation directory. You can download from here:"
						        " https://github.com/Visual-Attention-Network/VAN-Classification"
						    ),
						)
						parser.add_argument(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						__a           =							parser.parse_args()
						__a           =							args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						__a           =							args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
       import torch

       from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
           SpeechaTextaDecoder,
           SpeechaTextaForCausalLM,
       )


@require_torch
class 	a__						:

       def __init__( self   :  Dict   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict=9_9   ,				lowerCamelCase_   :  List[Any]=1_3   ,				lowerCamelCase_   :  Dict=1_6   ,				lowerCamelCase_   :  Any=7   ,				lowerCamelCase_   :  Dict=True   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Any=False   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  int=2   ,				lowerCamelCase_   :  Tuple=3_2   ,				lowerCamelCase_   :  int=4   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  str=3_0   ,				lowerCamelCase_   :  Optional[int]=0   ,				lowerCamelCase_   :  Optional[Any]=1   ,				lowerCamelCase_   :  Any=2   ,				lowerCamelCase_   :  Optional[int]=None   ,				):
            a_     :  Optional[Any]				      =     parent
            a_     :  Union[str, Any]				      =     batch_size
            a_     :  Tuple				      =     decoder_seq_length
            # For common tests
            a_     :  Union[str, Any]				      =     self.decoder_seq_length
            a_     :  List[str]				      =     is_training
            a_     :  int				      =     use_attention_mask
            a_     :  Dict				      =     use_labels

            a_     :  List[str]				      =     vocab_size
            a_     :  Optional[int]				      =     d_model
            a_     :  str				      =     d_model
            a_     :  Any				      =     decoder_layers
            a_     :  Tuple				      =     decoder_layers
            a_     :  Dict				      =     decoder_ffn_dim
            a_     :  List[str]				      =     decoder_attention_heads
            a_     :  List[Any]				      =     decoder_attention_heads
            a_     :  List[str]				      =     eos_token_id
            a_     :  Any				      =     bos_token_id
            a_     :  List[Any]				      =     pad_token_id
            a_     :  Dict				      =     decoder_start_token_id
            a_     :  Tuple				      =     use_cache
            a_     :  List[Any]				      =     max_position_embeddings

            a_     :  List[str]				      =     None
            a_     :  Any				      =     decoder_seq_length
            a_     :  List[Any]				      =     2
            a_     :  Optional[Any]				      =     1

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Any				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  Any				      =     None
            if self.use_attention_mask:
                 a_     :  Optional[Any]				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2     )

            a_     :  Tuple				      =     None
            if self.use_labels:
                 a_     :  int				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  int				      =     SpeechaTextaConfig(
                vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

            return (
                config,
                input_ids,
                attention_mask,
                lm_labels,
            )

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  List[str]   ,				):
            a_     :  List[Any]				      =     True
            a_     :  Optional[int]				      =     SpeechaTextaDecoder(config=lowerCamelCase_     ).to(lowerCamelCase_     ).eval()
            a_     :  Optional[int]				      =     input_ids[:2]

            input_ids[input_ids == 0] += 1
            # first forward pass
            a_     :  Union[str, Any]				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )
            a_     :  Tuple				      =     model(lowerCamelCase_     )
            a_     :  str				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )

            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     )     )
            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     ) + 1     )

            a_     :  Tuple				      =     outputs["""past_key_values"""]

            # create hypothetical next token and extent to next_input_ids
            a_     :  Any				      =     ids_tensor((2, 1)   ,				config.vocab_size - 1     ) + 1

            # append to next input_ids and
            a_     :  str				      =     torch.cat([input_ids, next_tokens]   ,				dim=-1     )

            print(lowerCamelCase_     )

            a_     :  Any				      =     model(lowerCamelCase_     )["""last_hidden_state"""]
            a_     :  int				      =     model(lowerCamelCase_   ,				past_key_values=lowerCamelCase_     )["""last_hidden_state"""]

            # select random slice
            a_     :  Tuple				      =     ids_tensor((1,)   ,				output_from_past.shape[-1]     ).item()
            a_     :  List[str]				      =     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
            a_     :  List[Any]				      =     output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            assert torch.allclose(lowerCamelCase_   ,				lowerCamelCase_   ,				atol=1E-3     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  List[Any]				      =     self.prepare_config_and_inputs()
            (
                (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      
            )     :  Optional[int]				      =     config_and_inputs

            a_     :  Tuple				      =     {
                """input_ids""": input_ids,
                """attention_mask""": attention_mask,
            }
            return config, inputs_dict


@require_torch
class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_    ,							lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  int				     =  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
       lowerCamelCase__:  Dict				     =  True
       lowerCamelCase__:  List[Any]				     =  False

       def     UpperCAmelCase( self   :  List[str]   ,				):
            a_     :  Optional[int]				      =     SpeechaTextaStandaloneDecoderModelTester(self   ,				is_training=lowerCamelCase_     )
            a_     :  str				      =     ConfigTester(self   ,				config_class=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  str     ):
            self.config_tester.run_common_tests()

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  int				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_     )

       def     UpperCAmelCase( self   :  int     ):
            return



