



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__snake_case						:Optional[int]      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''table-transformer'''
       UpperCamelCase__  :    Tuple											=						['''past_key_values''']
       UpperCamelCase__  :    List[str]											=						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=100     ,		__SCREAMING_SNAKE_CASE							:     Tuple=6     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     Any=8     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=6     ,		__SCREAMING_SNAKE_CASE							:     str=2_048     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=True     ,		__SCREAMING_SNAKE_CASE							:     Dict="relu"     ,		__SCREAMING_SNAKE_CASE							:     int=256     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.02     ,		__SCREAMING_SNAKE_CASE							:     Any=1.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     Dict="sine"     ,		__SCREAMING_SNAKE_CASE							:     str="resnet50"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=1     ,		__SCREAMING_SNAKE_CASE							:     Dict=5     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=5     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.1     ,		**__SCREAMING_SNAKE_CASE							:     str     ,		):


           '''simple docstring'''

           if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

           if not use_timm_backbone:
               if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                   __a						       =				CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
               elif isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
                   __a						       =				backbone_config.get('''model_type''')
                   __a						       =				CONFIG_MAPPING[backbone_model_type]
                   __a						       =				config_class.from_dict(__SCREAMING_SNAKE_CASE)
               # set timm attributes to None
               __a       ,   __a       ,   __a						       =				None, None, None

           __a						       =				use_timm_backbone
           __a						       =				backbone_config
           __a						       =				num_channels
           __a						       =				num_queries
           __a						       =				d_model
           __a						       =				encoder_ffn_dim
           __a						       =				encoder_layers
           __a						       =				encoder_attention_heads
           __a						       =				decoder_ffn_dim
           __a						       =				decoder_layers
           __a						       =				decoder_attention_heads
           __a						       =				dropout
           __a						       =				attention_dropout
           __a						       =				activation_dropout
           __a						       =				activation_function
           __a						       =				init_std
           __a						       =				init_xavier_std
           __a						       =				encoder_layerdrop
           __a						       =				decoder_layerdrop
           __a						       =				encoder_layers
           __a						       =				auxiliary_loss
           __a						       =				position_embedding_type
           __a						       =				backbone
           __a						       =				use_pretrained_backbone
           __a						       =				dilation
           # Hungarian matcher
           __a						       =				class_cost
           __a						       =				bbox_cost
           __a						       =				giou_cost
           # Loss coefficients
           __a						       =				mask_loss_coefficient
           __a						       =				dice_loss_coefficient
           __a						       =				bbox_loss_coefficient
           __a						       =				giou_loss_coefficient
           __a						       =				eos_coefficient
           super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.encoder_attention_heads





       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.d_model


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						version.parse('''1.11'''    )



       @property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                   ('''pixel_mask''', {0: '''batch'''}),
               ])



       @property
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           return 1E-5





       @property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return 12









'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
