



'''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


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

lowerCAmelCase_	:		Union[str, Any]			=     {
    """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 		SCREAMING_SNAKE_CASE     (					UpperCAmelCase__	):







				'''simple docstring'''
				UpperCAmelCase__							=							"gptj"
				UpperCAmelCase__							=							{
				    "max_position_embeddings": "n_positions",
				    "hidden_size": "n_embd",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}





				def __init__(						self					:						List[Any]       ,		lowercase__					:						str=50_400       ,		lowercase__					:						Optional[Any]=2_048       ,		lowercase__					:						List[str]=4_096       ,		lowercase__					:						List[str]=28       ,		lowercase__					:						List[str]=16       ,		lowercase__					:						List[str]=64       ,		lowercase__					:						Dict=None       ,		lowercase__					:						Optional[int]="gelu_new"       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						List[Any]=0.0       ,		lowercase__					:						Optional[int]=0.0       ,		lowercase__					:						Tuple=1e-5       ,		lowercase__					:						Tuple=0.0_2       ,		lowercase__					:						List[str]=True       ,		lowercase__					:						Tuple=50_256       ,		lowercase__					:						Dict=50_256       ,		lowercase__					:						List[str]=False       ,		**lowercase__					:						Optional[int]       ,		)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						vocab_size
										_UpperCamelCase			:			List[str]   			=						n_positions
										_UpperCamelCase			:			List[Any]   			=						n_embd
										_UpperCamelCase			:			str   			=						n_layer
										_UpperCamelCase			:			List[Any]   			=						n_head
										_UpperCamelCase			:			str   			=						n_inner
										_UpperCamelCase			:			List[Any]   			=						rotary_dim
										_UpperCamelCase			:			Optional[int]   			=						activation_function
										_UpperCamelCase			:			Any   			=						resid_pdrop
										_UpperCamelCase			:			str   			=						embd_pdrop
										_UpperCamelCase			:			List[str]   			=						attn_pdrop
										_UpperCamelCase			:			Any   			=						layer_norm_epsilon
										_UpperCamelCase			:			List[Any]   			=						initializer_range
										_UpperCamelCase			:			List[str]   			=						use_cache

										_UpperCamelCase			:			Dict   			=						bos_token_id
										_UpperCamelCase			:			str   			=						eos_token_id

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





class 		SCREAMING_SNAKE_CASE     (					UpperCAmelCase__	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						PretrainedConfig       ,		lowercase__					:						str = "default"       ,		lowercase__					:						List[PatchingSpec] = None       ,		lowercase__					:						bool = False       ,		)							->Optional[int]:


										'''simple docstring'''




										super().__init__(lowerCamelCase__       ,		task=lowerCamelCase__       ,		patching_specs=lowerCamelCase__       ,		use_past=lowerCamelCase__					)
										if not getattr(self._config       ,		"pad_token_id"       ,		lowerCamelCase__					):
																# TODO: how to do that better?
																_UpperCamelCase			:			Any   			=						0





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


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
										if self.use_past:
																self.fill_with_past_key_values_(lowerCamelCase__       ,		direction="inputs"					)
																_UpperCamelCase			:			Optional[Any]   			=						{0: "batch", 1: "past_sequence + sequence"}
										else:
																_UpperCamelCase			:			Dict   			=						{0: "batch", 1: "sequence"}

										return common_inputs





				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return self._config.n_layer





				@property
				def  snake_case__				(						self					:						Dict					)							->int:


										'''simple docstring'''




										return self._config.n_head





				def  snake_case__				(						self					:						int       ,		lowercase__					:						PreTrainedTokenizer       ,		lowercase__					:						int = -1       ,		lowercase__					:						int = -1       ,		lowercase__					:						bool = False       ,		lowercase__					:						Optional[TensorType] = None       ,		)							->Mapping[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						super(lowerCamelCase__       ,		self					).generate_dummy_inputs(
										    lowerCamelCase__       ,		batch_size=lowerCamelCase__       ,		seq_length=lowerCamelCase__       ,		is_pair=lowerCamelCase__       ,		framework=lowerCamelCase__					)

										# We need to order the input in the way they appears in the forward()
										_UpperCamelCase			:			Optional[Any]   			=						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			:			Tuple   			=						common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase			:			int   			=						seqlen + 2
																						_UpperCamelCase			:			Optional[Any]   			=						(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						_UpperCamelCase			:			Optional[int]   			=						[
																						    (torch.zeros(lowerCamelCase__					), torch.zeros(lowerCamelCase__					)) for _ in range(self.num_layers					)
																						]

										_UpperCamelCase			:			Optional[int]   			=						common_inputs["attention_mask"]
										if self.use_past:
																_UpperCamelCase			:			str   			=						ordered_inputs["attention_mask"].dtype
																_UpperCamelCase			:			List[str]   			=						torch.cat(
																    [ordered_inputs["attention_mask"], torch.ones(lowerCamelCase__       ,		lowerCamelCase__       ,		dtype=lowerCamelCase__					)]       ,		dim=1					)

										return ordered_inputs







				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return 13



'''simple docstring'''





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


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

lowerCAmelCase_	:		List[str]			=     {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''yolos'''





				def __init__(						self					:						int       ,		lowercase__					:						List[str]=768       ,		lowercase__					:						Optional[Any]=12       ,		lowercase__					:						Union[str, Any]=12       ,		lowercase__					:						Any=3_072       ,		lowercase__					:						List[Any]="gelu"       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Dict=0.0_2       ,		lowercase__					:						Tuple=1e-12       ,		lowercase__					:						str=[512, 864]       ,		lowercase__					:						Dict=16       ,		lowercase__					:						int=3       ,		lowercase__					:						Optional[Any]=True       ,		lowercase__					:						List[Any]=100       ,		lowercase__					:						str=True       ,		lowercase__					:						str=False       ,		lowercase__					:						List[str]=1       ,		lowercase__					:						Dict=5       ,		lowercase__					:						str=2       ,		lowercase__					:						Optional[int]=5       ,		lowercase__					:						Optional[int]=2       ,		lowercase__					:						Optional[Any]=0.1       ,		**lowercase__					:						Union[str, Any]       ,		)							->Tuple:


										'''simple docstring'''




										super().__init__(**lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						hidden_size
										_UpperCamelCase			:			str   			=						num_hidden_layers
										_UpperCamelCase			:			Optional[Any]   			=						num_attention_heads
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			Union[str, Any]   			=						hidden_act
										_UpperCamelCase			:			Tuple   			=						hidden_dropout_prob
										_UpperCamelCase			:			Any   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						initializer_range
										_UpperCamelCase			:			str   			=						layer_norm_eps
										_UpperCamelCase			:			Optional[int]   			=						image_size
										_UpperCamelCase			:			int   			=						patch_size
										_UpperCamelCase			:			Dict   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						qkv_bias
										_UpperCamelCase			:			Dict   			=						num_detection_tokens
										_UpperCamelCase			:			int   			=						use_mid_position_embeddings
										_UpperCamelCase			:			int   			=						auxiliary_loss
										# Hungarian matcher
										_UpperCamelCase			:			Tuple   			=						class_cost
										_UpperCamelCase			:			str   			=						bbox_cost
										_UpperCamelCase			:			str   			=						giou_cost
										# Loss coefficients
										_UpperCamelCase			:			List[str]   			=						bbox_loss_coefficient
										_UpperCamelCase			:			str   			=						giou_loss_coefficient
										_UpperCamelCase			:			str   			=						eos_coefficient





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							version.parse('''1.11'''	)





				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




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





				@property
				def  snake_case__				(						self					:						Any					)							->float:


										'''simple docstring'''




										return 1e-4







				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return 12


