



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


A__  :  Tuple								=					logging.get_logger(__name__)

A__  :  str								=					{
    '''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 				__snake_case						(     A__						):
							_a			      = '''bart'''
							_a			      = ['''past_key_values''']
							_a			      = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




							def __init__(       self     :       str				,				A_     :       Any=5_0_2_6_5				,				A_     :       Optional[Any]=1_0_2_4				,				A_     :       int=1_2				,				A_     :       Union[str, Any]=4_0_9_6				,				A_     :       Any=1_6				,				A_     :       List[str]=1_2				,				A_     :       Tuple=4_0_9_6				,				A_     :       str=1_6				,				A_     :       List[Any]=0.0				,				A_     :       List[str]=0.0				,				A_     :       str="gelu"				,				A_     :       Optional[Any]=1_0_2_4				,				A_     :       str=0.1				,				A_     :       int=0.0				,				A_     :       Any=0.0				,				A_     :       Dict=0.02				,				A_     :       Optional[int]=0.0				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       List[str]=3				,				A_     :       int=1				,				A_     :       List[str]=0				,				A_     :       Optional[Any]=2				,				A_     :       List[str]=True				,				A_     :       int=2				,				A_     :       str=2				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     int									= vocab_size
									lowerCAmelCase_			:     List[str]									= max_position_embeddings
									lowerCAmelCase_			:     int									= d_model
									lowerCAmelCase_			:     Tuple									= encoder_ffn_dim
									lowerCAmelCase_			:     List[Any]									= encoder_layers
									lowerCAmelCase_			:     int									= encoder_attention_heads
									lowerCAmelCase_			:     Optional[Any]									= decoder_ffn_dim
									lowerCAmelCase_			:     Optional[int]									= decoder_layers
									lowerCAmelCase_			:     List[str]									= decoder_attention_heads
									lowerCAmelCase_			:     List[Any]									= dropout
									lowerCAmelCase_			:     Dict									= attention_dropout
									lowerCAmelCase_			:     Any									= activation_dropout
									lowerCAmelCase_			:     int									= activation_function
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     Optional[Any]									= encoder_layerdrop
									lowerCAmelCase_			:     int									= decoder_layerdrop
									lowerCAmelCase_			:     str									= classifier_dropout
									lowerCAmelCase_			:     str									= use_cache
									lowerCAmelCase_			:     Union[str, Any]									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= scale_embedding  # scale factor will be sqrt(d_model) if True

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

									# ensure backward compatibility for BART CNN models
									if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				,				UpperCamelCase_):
											lowerCAmelCase_			:     Optional[int]									= 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 				__snake_case						(     A__						):




							@property
							def    UpperCAmelCase__			(       self     :       Dict):


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

											if self.use_past:
													lowerCAmelCase_			:     Optional[Any]									= {0: '''batch'''}
													lowerCAmelCase_			:     int									= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
													lowerCAmelCase_			:     List[Any]									= {0: '''batch''', 1: '''decoder_sequence'''}
													lowerCAmelCase_			:     int									= {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
													self.fill_with_past_key_values_(UpperCamelCase_				,				direction='''inputs''')
									elif self.task == "causal-lm":
											# TODO: figure this case out.
											lowerCAmelCase_			:     List[str]									= OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ])
											if self.use_past:
													lowerCAmelCase_			:     Optional[Any]									= self.num_layers
													for i in range(UpperCamelCase_):
															lowerCAmelCase_			:     Tuple									= {0: '''batch''', 2: '''past_sequence + sequence'''}
															lowerCAmelCase_			:     List[str]									= {0: '''batch''', 2: '''past_sequence + sequence'''}
									else:
											lowerCAmelCase_			:     int									= 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    UpperCAmelCase__			(       self     :       int):


									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_			:     Optional[Any]									= super().outputs
									else:
											lowerCAmelCase_			:     List[str]									= super(UpperCamelCase_				,				self).outputs
											if self.use_past:
													lowerCAmelCase_			:     Union[str, Any]									= self.num_layers
													for i in range(UpperCamelCase_):
															lowerCAmelCase_			:     Any									= {0: '''batch''', 2: '''past_sequence + sequence'''}
															lowerCAmelCase_			:     Union[str, Any]									= {0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs




							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       Union[str, Any]				,				A_     :       Optional[Any] = -1				,				A_     :       Tuple = -1				,				A_     :       str = False				,				A_     :       Tuple = None				,				):
									lowerCAmelCase_			:     Dict									= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)

									# Generate decoder inputs
									lowerCAmelCase_			:     Optional[Any]									= seq_length if not self.use_past else 1
									lowerCAmelCase_			:     Optional[Any]									= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)
									lowerCAmelCase_			:     Optional[Any]									= {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									lowerCAmelCase_			:     int									= dict(**UpperCamelCase_				,				**UpperCamelCase_)

									if self.use_past:
											if not is_torch_available():
													raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
											else:
													import torch
											lowerCAmelCase_			:     List[Any]									= common_inputs['''input_ids'''].shape
											lowerCAmelCase_			:     Optional[int]									= common_inputs['''decoder_input_ids'''].shape[1]
											lowerCAmelCase_			:     Union[str, Any]									= self.num_attention_heads
											lowerCAmelCase_			:     Tuple									= (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											lowerCAmelCase_			:     Any									= decoder_seq_length + 3
											lowerCAmelCase_			:     str									= (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											lowerCAmelCase_			:     str									= torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_				,				UpperCamelCase_)]				,				dim=1)

											lowerCAmelCase_			:     Union[str, Any]									= []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											lowerCAmelCase_			:     Any									= self.num_layers
											lowerCAmelCase_			:     Optional[int]									= min(UpperCamelCase_				,				UpperCamelCase_)
											lowerCAmelCase_			:     Any									= max(UpperCamelCase_				,				UpperCamelCase_) - min_num_layers
											lowerCAmelCase_			:     Tuple									= '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Any				,				A_     :       str = -1				,				A_     :       Tuple = -1				,				A_     :       str = False				,				A_     :       Dict = None				,				):
									lowerCAmelCase_			:     Union[str, Any]									= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)

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

											lowerCAmelCase_			:     Optional[Any]									= common_inputs['''attention_mask'''].dtype
											lowerCAmelCase_			:     int									= torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_				,				UpperCamelCase_				,				dtype=UpperCamelCase_)]				,				dim=1)
											lowerCAmelCase_			:     Any									= [
											    (torch.zeros(UpperCamelCase_), torch.zeros(UpperCamelCase_)) for _ in range(UpperCamelCase_)
											]
									return common_inputs




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Any				,				A_     :       Optional[int] = -1				,				A_     :       Dict = -1				,				A_     :       Dict = False				,				A_     :       int = None				,				):
									lowerCAmelCase_			:     Tuple									= compute_effective_axis_dimension(
									    UpperCamelCase_				,				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
									lowerCAmelCase_			:     Optional[Any]									= tokenizer.num_special_tokens_to_add(UpperCamelCase_)
									lowerCAmelCase_			:     Optional[int]									= compute_effective_axis_dimension(
									    UpperCamelCase_				,				fixed_dimension=OnnxConfig.default_fixed_sequence				,				num_token_to_add=UpperCamelCase_)

									# Generate dummy inputs according to compute batch and sequence
									lowerCAmelCase_			:     Tuple									= [''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
									lowerCAmelCase_			:     str									= dict(tokenizer(UpperCamelCase_				,				return_tensors=UpperCamelCase_))
									return common_inputs




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       int				,				A_     :       Union[str, Any] = -1				,				A_     :       List[Any] = -1				,				A_     :       Union[str, Any] = False				,				A_     :       List[Any] = None				,				):


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

									elif self.task == "causal-lm":
											lowerCAmelCase_			:     Any									= self._generate_dummy_inputs_for_causal_lm(
											    UpperCamelCase_				,				batch_size=UpperCamelCase_				,				seq_length=UpperCamelCase_				,				is_pair=UpperCamelCase_				,				framework=UpperCamelCase_)
									else:
											lowerCAmelCase_			:     Union[str, Any]									= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    UpperCamelCase_				,				batch_size=UpperCamelCase_				,				seq_length=UpperCamelCase_				,				is_pair=UpperCamelCase_				,				framework=UpperCamelCase_)

									return common_inputs







							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       List[Any]				,				A_     :       str				,				A_     :       Tuple				,				A_     :       Any):


									if self.task in ["default", "seq2seq-lm"]:
											lowerCAmelCase_			:     List[Any]									= super()._flatten_past_key_values_(UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)
									else:
											lowerCAmelCase_			:     Any									= super(UpperCamelCase_				,				self)._flatten_past_key_values_(
											    UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)









'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case							      =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case							      =     []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
		)
		rename_keys.append(
		    (
		        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
		        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
		    )
		)
		rename_keys.append(
		    (
		        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
		        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
		    )
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
		)
		rename_keys.append(
		    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
		)
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
		rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			a      (			__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= state_dict.pop(__a   )
					UpperCamelCase__   :int												= val


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															UpperCamelCase__   :Dict												= key.replace('''backbone.0.body''' ,					'''backbone.conv_encoder.model'''   )
															UpperCamelCase__   :List[str]												= value
										else:
															UpperCamelCase__   :Dict												= value

					return new_state_dict


def 			a      (			__a   )  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= ''''''

					# first: transformer encoder
					for i in range(6   ):
										# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
										UpperCamelCase__   :Optional[Any]												= state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''   )
										UpperCamelCase__   :str												= state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''   )
										# next, add query, keys and values (in that order) to the state dict
										UpperCamelCase__   :Any												= in_proj_weight[:256, :]
										UpperCamelCase__   :Tuple												= in_proj_bias[:256]
										UpperCamelCase__   :Optional[int]												= in_proj_weight[256:512, :]
										UpperCamelCase__   :Optional[Any]												= in_proj_bias[256:512]
										UpperCamelCase__   :Tuple												= in_proj_weight[-256:, :]
										UpperCamelCase__   :Dict												= in_proj_bias[-256:]
					# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
					for i in range(6   ):
										# read in weights + bias of input projection layer of self-attention
										UpperCamelCase__   :List[str]												= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
										UpperCamelCase__   :Optional[Any]												= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''   )
										# next, add query, keys and values (in that order) to the state dict
										UpperCamelCase__   :Any												= in_proj_weight[:256, :]
										UpperCamelCase__   :Optional[int]												= in_proj_bias[:256]
										UpperCamelCase__   :Tuple												= in_proj_weight[256:512, :]
										UpperCamelCase__   :Dict												= in_proj_bias[256:512]
										UpperCamelCase__   :Any												= in_proj_weight[-256:, :]
										UpperCamelCase__   :Dict												= in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										UpperCamelCase__   :List[str]												= state_dict.pop(
										    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''   )
										UpperCamelCase__   :Any												= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''   )
										# next, add query, keys and values (in that order) of cross-attention to the state dict
										UpperCamelCase__   :Optional[Any]												= in_proj_weight_cross_attn[:256, :]
										UpperCamelCase__   :Any												= in_proj_bias_cross_attn[:256]
										UpperCamelCase__   :Any												= in_proj_weight_cross_attn[256:512, :]
										UpperCamelCase__   :Dict												= in_proj_bias_cross_attn[256:512]
										UpperCamelCase__   :str												= in_proj_weight_cross_attn[-256:, :]
										UpperCamelCase__   :Tuple												= in_proj_bias_cross_attn[-256:]


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



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :str												= image.size
					UpperCamelCase__   :Optional[Any]												= max(__a ,					__a   )
					UpperCamelCase__   :List[Any]												= 800 if '''detection''' in checkpoint_url else 1000
					UpperCamelCase__   :Dict												= target_max_size / current_max_size
					UpperCamelCase__   :Any												= image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

					return resized_image


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :Any												= F.to_tensor(__a   )
					UpperCamelCase__   :int												= F.normalize(__a ,					mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] ,					std=[0.2_2_9, 0.2_2_4, 0.2_2_5]   )
					return image


@torch.no_grad()
def 			a      (			__a ,					__a ,					__a   )  ->	Dict:



					'''simple docstring'''






					logger.info('''Converting model...'''   )

					# load original state dict
					UpperCamelCase__   :Optional[Any]												= torch.hub.load_state_dict_from_url(__a ,					map_location='''cpu'''   )
					# rename keys
					for src, dest in rename_keys:
										rename_key(__a ,					__a ,					__a   )
					UpperCamelCase__   :Any												= rename_backbone_keys(__a   )
					# query, key and value matrices need special treatment
					read_in_q_k_v(__a   )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					UpperCamelCase__   :Dict												= '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
															UpperCamelCase__   :Optional[Any]												= state_dict.pop(__a   )
															UpperCamelCase__   :int												= val
    # create HuggingFace model and load state dict
					UpperCamelCase__   :str												= TableTransformerConfig(
					    backbone='''resnet18''' ,					mask_loss_coefficient=1 ,					dice_loss_coefficient=1 ,					ce_loss_coefficient=1 ,					bbox_loss_coefficient=5 ,					giou_loss_coefficient=2 ,					eos_coefficient=0.4 ,					class_cost=1 ,					bbox_cost=5 ,					giou_cost=2 ,					)

					if "detection" in checkpoint_url:
										UpperCamelCase__   :List[str]												= 15
										UpperCamelCase__   :int												= 2
										UpperCamelCase__   :Tuple												= {0: '''table''', 1: '''table rotated'''}
										UpperCamelCase__   :int												= idalabel
										UpperCamelCase__   :Dict												= {v: k for k, v in idalabel.items()}
					else:
										UpperCamelCase__   :int												= 125
										UpperCamelCase__   :List[str]												= 6
										UpperCamelCase__   :Optional[Any]												= {
										    0: '''table''',
										    1: '''table column''',
										    2: '''table row''',
										    3: '''table column header''',
										    4: '''table projected row header''',
										    5: '''table spanning cell''',
										}
										UpperCamelCase__   :Dict												= idalabel
										UpperCamelCase__   :Optional[Any]												= {v: k for k, v in idalabel.items()}

					UpperCamelCase__   :List[Any]												= DetrImageProcessor(
					    format='''coco_detection''' ,					max_size=800 if '''detection''' in checkpoint_url else 1000   )
					UpperCamelCase__   :int												= TableTransformerForObjectDetection(__a   )
					model.load_state_dict(__a   )
					model.eval()

					# verify our conversion
					UpperCamelCase__   :Dict												= '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
					UpperCamelCase__   :Optional[Any]												= hf_hub_download(repo_id='''nielsr/example-pdf''' ,					repo_type='''dataset''' ,					filename=__a   )
					UpperCamelCase__   :Tuple												= Image.open(__a   ).convert('''RGB'''   )
					UpperCamelCase__   :int												= normalize(resize(__a ,					__a   )   ).unsqueeze(0   )

					UpperCamelCase__   :Optional[int]												= model(__a   )

					if "detection" in checkpoint_url:
										UpperCamelCase__   :Dict												= (1, 15, 3)
										UpperCamelCase__   :List[Any]												= torch.tensor(
										    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]   )
										UpperCamelCase__   :Tuple												= torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]   )

					else:
										UpperCamelCase__   :Optional[Any]												= (1, 125, 7)
										UpperCamelCase__   :Dict												= torch.tensor(
										    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]   )
										UpperCamelCase__   :List[Any]												= torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]   )

					assert outputs.logits.shape == expected_shape
					assert torch.allclose(outputs.logits[0, :3, :3] ,					__a ,					atol=1e-4   )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,					__a ,					atol=1e-4   )
					print('''Looks ok!'''   )

					if pytorch_dump_folder_path is not None:
										# Save model and image processor
										logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
										Path(__a   ).mkdir(exist_ok=__a   )
										model.save_pretrained(__a   )
										image_processor.save_pretrained(__a   )

					if push_to_hub:
										# Push model to HF hub
										logger.info('''Pushing model to the hub...'''   )
										UpperCamelCase__   :Union[str, Any]												= (
										    '''microsoft/table-transformer-detection'''
										    if '''detection''' in checkpoint_url
										    else '''microsoft/table-transformer-structure-recognition'''
										)
										model.push_to_hub(__a   )
										image_processor.push_to_hub(__a   )


if __name__ == "__main__":
		__snake_case							      =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
		    type=str,
		    choices=[
		        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
		        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
		    ],
		    help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		__snake_case							      =     parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)