
"""simple docstring"""

import copy
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


UpperCamelCase			:       Union[str, Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       Any									=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"conditional_detr"
     lowercase		     =		["past_key_values"]
     lowercase		     =		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }





     def __init__(			self			,  __UpperCAmelCase=True			,  __UpperCAmelCase=None			,  __UpperCAmelCase=3			,  __UpperCAmelCase=300			,  __UpperCAmelCase=6			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=8			,  __UpperCAmelCase=6			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=8			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=256			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase="sine"			,  __UpperCAmelCase="resnet50"			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=2			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=1			,  __UpperCAmelCase=1			,  __UpperCAmelCase=2			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.2_5			,  **__UpperCAmelCase			,  ):

      '''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.'   )
        __UpperCamelCase       						= CONFIG_MAPPING['resnet'](out_features=['stage4']   )
       elif isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= backbone_config.get('model_type'   )
        __UpperCamelCase       						= CONFIG_MAPPING[backbone_model_type]
        __UpperCamelCase       						= config_class.from_dict(__UpperCAmelCase   )

      __UpperCamelCase       						= use_timm_backbone
      __UpperCamelCase       						= backbone_config
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= num_queries
      __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       						= init_xavier_std
      __UpperCamelCase       						= encoder_layerdrop
      __UpperCamelCase       						= decoder_layerdrop
      __UpperCamelCase       						= encoder_layers
      __UpperCamelCase       						= auxiliary_loss
      __UpperCamelCase       						= position_embedding_type
      __UpperCamelCase       						= backbone
      __UpperCamelCase       						= use_pretrained_backbone
      __UpperCamelCase       						= dilation
      # Hungarian matcher
      __UpperCamelCase       						= class_cost
      __UpperCamelCase       						= bbox_cost
      __UpperCamelCase       						= giou_cost
      # Loss coefficients
      __UpperCamelCase       						= mask_loss_coefficient
      __UpperCamelCase       						= dice_loss_coefficient
      __UpperCamelCase       						= cls_loss_coefficient
      __UpperCamelCase       						= bbox_loss_coefficient
      __UpperCamelCase       						= giou_loss_coefficient
      __UpperCamelCase       						= focal_alpha
      super().__init__(is_encoder_decoder=__UpperCAmelCase			,  **__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.encoder_attention_heads





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.d_model







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= copy.deepcopy(self.__dict__   )
      if self.backbone_config is not None:
       __UpperCamelCase       						= self.backbone_config.to_dict()
      __UpperCamelCase       						= self.__class__.model_type
      return output


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		version.parse("1.11"    )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 1E-5







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 12





"""simple docstring"""

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

      super().__init__(
          bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  tie_word_embeddings=__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= OrderedDict({'input_ids': common_inputs['input_ids']}   )

      # Need to add the past_keys
      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       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



