
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[Any]	=		"bart"
						UpperCamelCase_							: Optional[Any]	=		["past_key_values"]
						UpperCamelCase_							: Any	=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

						def __init__(			self       ,							a=5_02_65       ,							a=10_24       ,							a=12       ,							a=40_96       ,							a=16       ,							a=12       ,							a=40_96       ,							a=16       ,							a=0.0       ,							a=0.0       ,							a="gelu"       ,							a=10_24       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=0.0       ,							a=False       ,							a=True       ,							a=3       ,							a=1       ,							a=0       ,							a=2       ,							a=True       ,							a=2       ,							a=2       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	vocab_size
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	decoder_layerdrop
								_UpperCamelCase						    =	classifier_dropout
								_UpperCamelCase						    =	use_cache
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    num_labels=a       ,							pad_token_id=a       ,							bos_token_id=a       ,							eos_token_id=a       ,							is_encoder_decoder=a       ,							decoder_start_token_id=a       ,							forced_eos_token_id=a       ,							**a       ,							)

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""       ,							a				):
										_UpperCamelCase						    =	self.bos_token_id
										warnings.warn(
										    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
										    """The config can simply be saved and uploaded again to be fixed."""				)







class        lowerCAmelCase__      (   __lowercase	):

						@property
						def  A_				(			self				)					->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
										_UpperCamelCase						    =	OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]				)

										if self.use_past:
												_UpperCamelCase						    =	{0: """batch"""}
												_UpperCamelCase						    =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
												_UpperCamelCase						    =	{0: """batch""", 1: """decoder_sequence"""}
												_UpperCamelCase						    =	{0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
												self.fill_with_past_key_values_(a       ,							direction="""inputs"""				)
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										_UpperCamelCase						    =	OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]				)
										if self.use_past:
												_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_layers
												for i in range(a				):
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
								else:
										_UpperCamelCase						    =	OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
										        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
										    ]				)

								return common_inputs

						@property
						def  A_				(			self				)					->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
										_UpperCamelCase						    =	super().outputs
								else:
										_UpperCamelCase						    =	super(a       ,							self				).outputs
										if self.use_past:
												_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_layers
												for i in range(a				):
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
														_UpperCamelCase						    =	{0: """batch""", 2: """past_sequence + sequence"""}
								return common_outputs

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a       ,							a       ,							a       ,							a       ,							a				)

								# Generate decoder inputs
								_UpperCamelCase						    =	seq_length if not self.use_past else 1
								_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a       ,							a       ,							a       ,							a       ,							a				)
								_UpperCamelCase						    =	{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								_UpperCamelCase						    =	dict(**a       ,							**a				)

								if self.use_past:
										if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
										else:
												import torch
										_UpperCamelCase    ,	_UpperCamelCase						    =	common_inputs["""input_ids"""].shape
										_UpperCamelCase						    =	common_inputs["""decoder_input_ids"""].shape[1]
										_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_attention_heads
										_UpperCamelCase						    =	(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										_UpperCamelCase						    =	decoder_seq_length + 3
										_UpperCamelCase						    =	(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										_UpperCamelCase						    =	torch.cat(
										    [common_inputs["""decoder_attention_mask"""], torch.ones(a       ,							a				)]       ,							dim=1				)

										_UpperCamelCase						    =	[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_layers
										_UpperCamelCase						    =	min(a       ,							a				)
										_UpperCamelCase						    =	max(a       ,							a				) - min_num_layers
										_UpperCamelCase						    =	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

										for _ in range(a				):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(a				),
												        torch.zeros(a				),
												        torch.zeros(a				),
												        torch.zeros(a				),
												    )				)
										# TODO: test this.
										_UpperCamelCase						    =	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
										for _ in range(a       ,							a				):
												common_inputs["past_key_values"].append((torch.zeros(a				), torch.zeros(a				))				)
								return common_inputs

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a       ,							a       ,							a       ,							a       ,							a				)

								if self.use_past:
										if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
										else:
												import torch
										_UpperCamelCase    ,	_UpperCamelCase						    =	common_inputs["""input_ids"""].shape
										# Not using the same length for past_key_values
										_UpperCamelCase						    =	seqlen + 2
										_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_layers
										_UpperCamelCase    ,	_UpperCamelCase						    =	self.num_attention_heads
										_UpperCamelCase						    =	(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										_UpperCamelCase						    =	common_inputs["""attention_mask"""].dtype
										_UpperCamelCase						    =	torch.cat(
										    [common_inputs["""attention_mask"""], torch.ones(a       ,							a       ,							dtype=a				)]       ,							dim=1				)
										_UpperCamelCase						    =	[
										    (torch.zeros(a				), torch.zeros(a				)) for _ in range(a				)
										]
								return common_inputs

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	compute_effective_axis_dimension(
								    a       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0				)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								_UpperCamelCase						    =	tokenizer.num_special_tokens_to_add(a				)
								_UpperCamelCase						    =	compute_effective_axis_dimension(
								    a       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=a				)

								# Generate dummy inputs according to compute batch and sequence
								_UpperCamelCase						    =	[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
								_UpperCamelCase						    =	dict(tokenizer(a       ,							return_tensors=a				)				)
								return common_inputs

						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							)					->      Mapping[str, Any]:


								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
										_UpperCamelCase						    =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    a       ,							batch_size=a       ,							seq_length=a       ,							is_pair=a       ,							framework=a				)

								elif self.task == "causal-lm":
										_UpperCamelCase						    =	self._generate_dummy_inputs_for_causal_lm(
										    a       ,							batch_size=a       ,							seq_length=a       ,							is_pair=a       ,							framework=a				)
								else:
										_UpperCamelCase						    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a       ,							batch_size=a       ,							seq_length=a       ,							is_pair=a       ,							framework=a				)

								return common_inputs







						def  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
										_UpperCamelCase						    =	super()._flatten_past_key_values_(a       ,							a       ,							a       ,							a				)
								else:
										_UpperCamelCase						    =	super(a       ,							self				)._flatten_past_key_values_(
										    a       ,							a       ,							a       ,							a				)





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

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

						def __call__(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook				)				)
								self.module(a				)
								[x.remove() for x in self.handles]
								return self







						@property
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				):
										raise Exception(
										    F'Numbers of operations are different. Source module has {len(a				)} operations while'
										    F' destination module has {len(a				)}.'				)

								for dest_m, src_m in zip(a       ,							a				):
										dest_m.load_state_dict(src_m.state_dict()				)
										if self.verbose == 1:
												print(F'Transfered from={src_m} to={dest_m}'				)




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

		assert torch.allclose(from_model(lowerCAmelCase )						,  our_model(lowerCAmelCase ).logits ), "The model logits don't match the original one."

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

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

				# we can use the convnext one
				_UpperCamelCase						    =	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=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

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

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

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		}

		if model_name:
				convert_weight_and_push(lowerCAmelCase						,  names_to_config[model_name]						,  lowerCAmelCase						,  lowerCAmelCase )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase__	=							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: resnet18,26,34,50,101,152. 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.",
		)

		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							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)



