
"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def __A						(   a_ :							List[str]     )->							Tuple:




		'''simple docstring'''






		if "img_encoder.pos_embed" in name:
				SCREAMING_SNAKE_CASE       :     str				= name.replace('''img_encoder.pos_embed'''		,		'''vision_model.embeddings.position_embeddings'''     )
		if "img_encoder.patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE       :     Tuple				= name.replace('''img_encoder.patch_embed.proj'''		,		'''vision_model.embeddings.patch_embeddings.projection'''     )
		if "img_encoder.patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE       :     Tuple				= name.replace('''img_encoder.patch_embed.norm'''		,		'''vision_model.embeddings.layernorm'''     )
		if "img_encoder.layers" in name:
				SCREAMING_SNAKE_CASE       :     Tuple				= name.replace('''img_encoder.layers'''		,		'''vision_model.encoder.stages'''     )
		if "blocks" in name and "res" not in name:
				SCREAMING_SNAKE_CASE       :     Dict				= name.replace('''blocks'''		,		'''layers'''     )
		if "attn" in name and "pre_assign" not in name:
				SCREAMING_SNAKE_CASE       :     Any				= name.replace('''attn'''		,		'''self_attn'''     )
		if "proj" in name and "self_attn" in name and "text" not in name:
				SCREAMING_SNAKE_CASE       :     Any				= name.replace('''proj'''		,		'''out_proj'''     )
		if "pre_assign_attn.attn.proj" in name:
				SCREAMING_SNAKE_CASE       :     int				= name.replace('''pre_assign_attn.attn.proj'''		,		'''pre_assign_attn.attn.out_proj'''     )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= name.replace('''norm1'''		,		'''layer_norm1'''     )
		if "norm2" in name and "pre_assign" not in name:
				SCREAMING_SNAKE_CASE       :     Dict				= name.replace('''norm2'''		,		'''layer_norm2'''     )
		if "img_encoder.norm" in name:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= name.replace('''img_encoder.norm'''		,		'''vision_model.layernorm'''     )
		# text encoder
		if "text_encoder.token_embedding" in name:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= name.replace('''text_encoder.token_embedding'''		,		'''text_model.embeddings.token_embedding'''     )
		if "text_encoder.positional_embedding" in name:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= name.replace('''text_encoder.positional_embedding'''		,		'''text_model.embeddings.position_embedding.weight'''     )
		if "text_encoder.transformer.resblocks." in name:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= name.replace('''text_encoder.transformer.resblocks.'''		,		'''text_model.encoder.layers.'''     )
		if "ln_1" in name:
				SCREAMING_SNAKE_CASE       :     List[Any]				= name.replace('''ln_1'''		,		'''layer_norm1'''     )
		if "ln_2" in name:
				SCREAMING_SNAKE_CASE       :     str				= name.replace('''ln_2'''		,		'''layer_norm2'''     )
		if "c_fc" in name:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= name.replace('''c_fc'''		,		'''fc1'''     )
		if "c_proj" in name:
				SCREAMING_SNAKE_CASE       :     Tuple				= name.replace('''c_proj'''		,		'''fc2'''     )
		if "text_encoder" in name:
				SCREAMING_SNAKE_CASE       :     int				= name.replace('''text_encoder'''		,		'''text_model'''     )
		if "ln_final" in name:
				SCREAMING_SNAKE_CASE       :     List[str]				= name.replace('''ln_final'''		,		'''final_layer_norm'''     )
		# projection layers
		if "img_projector.linear_hidden." in name:
				SCREAMING_SNAKE_CASE       :     List[str]				= name.replace('''img_projector.linear_hidden.'''		,		'''visual_projection.'''     )
		if "img_projector.linear_out." in name:
				SCREAMING_SNAKE_CASE       :     List[Any]				= name.replace('''img_projector.linear_out.'''		,		'''visual_projection.3.'''     )
		if "text_projector.linear_hidden" in name:
				SCREAMING_SNAKE_CASE       :     Any				= name.replace('''text_projector.linear_hidden'''		,		'''text_projection'''     )
		if "text_projector.linear_out" in name:
				SCREAMING_SNAKE_CASE       :     Dict				= name.replace('''text_projector.linear_out'''		,		'''text_projection.3'''     )

		return name





def __A						(   a_ :							Any		,		a_ :							List[str]     )->							Any:




		'''simple docstring'''






		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= orig_state_dict.pop(a_     )

				if "qkv" in key:
						# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						SCREAMING_SNAKE_CASE       :     List[str]				= key.split('''.'''     )
						SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= int(key_split[2]     ), int(key_split[4]     )
						SCREAMING_SNAKE_CASE       :     Any				= config.vision_config.hidden_size
						if "weight" in key:
								SCREAMING_SNAKE_CASE       :     str				= val[:dim, :]
								SCREAMING_SNAKE_CASE       :     List[str]				= val[dim : dim * 2, :]
								SCREAMING_SNAKE_CASE       :     Union[str, Any]				= val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE       :     str				= val[:dim]
								SCREAMING_SNAKE_CASE       :     Dict				= val[dim : dim * 2]
								SCREAMING_SNAKE_CASE       :     Union[str, Any]				= val[-dim:]
				elif "in_proj" in key:
						# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						SCREAMING_SNAKE_CASE       :     Any				= key.split('''.'''     )
						SCREAMING_SNAKE_CASE       :     Any				= int(key_split[3]     )
						SCREAMING_SNAKE_CASE       :     List[str]				= config.text_config.hidden_size
						if "weight" in key:
								SCREAMING_SNAKE_CASE       :     Tuple				= val[:dim, :]
								SCREAMING_SNAKE_CASE       :     str				= val[
								    dim : dim * 2, :
								]
								SCREAMING_SNAKE_CASE       :     List[str]				= val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE       :     Tuple				= val[:dim]
								SCREAMING_SNAKE_CASE       :     Any				= val[dim : dim * 2]
								SCREAMING_SNAKE_CASE       :     List[Any]				= val[-dim:]
				else:
						SCREAMING_SNAKE_CASE       :     List[Any]				= rename_key(a_     )
						# squeeze if necessary
						if (
						    "text_projection.0" in new_name
						    or "text_projection.3" in new_name
						    or "visual_projection.0" in new_name
						    or "visual_projection.3" in new_name
						):
								SCREAMING_SNAKE_CASE       :     Union[str, Any]				= val.squeeze_()
						else:
								SCREAMING_SNAKE_CASE       :     Dict				= val

		return orig_state_dict





def __A						(   )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE       :     Any				= Image.open(requests.get(a_		,		stream=a_     ).raw     )
		return im





@torch.no_grad()
def __A						(   a_ :							List[Any]		,		a_ :							Any		,		a_ :							Any="groupvit-gcc-yfcc"		,		a_ :							Optional[int]=False     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= GroupViTConfig()
		SCREAMING_SNAKE_CASE       :     Optional[int]				= GroupViTModel(a_     ).eval()

		SCREAMING_SNAKE_CASE       :     str				= torch.load(a_		,		map_location='''cpu'''     )['''model''']
		SCREAMING_SNAKE_CASE       :     str				= convert_state_dict(a_		,		a_     )
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= model.load_state_dict(a_		,		strict=a_     )
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a_     ) == 0)

		# verify result
		SCREAMING_SNAKE_CASE       :     Tuple				= CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''     )
		SCREAMING_SNAKE_CASE       :     List[str]				= prepare_img()
		SCREAMING_SNAKE_CASE       :     List[str]				= processor(text=['''a photo of a cat''', '''a photo of a dog''']		,		images=a_		,		padding=a_		,		return_tensors='''pt'''     )

		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     str				= model(**a_     )

		if model_name == "groupvit-gcc-yfcc":
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([[13.3523, 6.3629]]     )
		elif model_name == "groupvit-gcc-redcaps":
				SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([[16.1873, 8.6230]]     )
		else:
				raise ValueError(F"Model name {model_name} not supported."     )
		assert torch.allclose(outputs.logits_per_image		,		a_		,		atol=1E-3     )

		processor.save_pretrained(a_     )
		model.save_pretrained(a_     )
		print('''Successfully saved processor and model to'''		,		a_     )

		if push_to_hub:
				print('''Pushing to the hub...'''     )
				processor.push_to_hub(a_		,		organization='''nielsr'''     )
				model.push_to_hub(a_		,		organization='''nielsr'''     )


if __name__ == "__main__":
					lowerCamelCase__ :       str						     =			argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
					)
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
					parser.add_argument(
					    "--model_name",
					    default="groupvit-gccy-fcc",
					    type=str,
					    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
					)
					lowerCamelCase__ :       Union[str, Any]						     =			parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""






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

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """blenderbot-small"""
	UpperCamelCase				=  ["""past_key_values"""]
	UpperCamelCase				=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(						self    :Any		, lowerCamelCase_    :Dict=5_02_65		, lowerCamelCase_    :str=5_12		, lowerCamelCase_    :Tuple=8		, lowerCamelCase_    :int=20_48		, lowerCamelCase_    :str=16		, lowerCamelCase_    :Optional[int]=8		, lowerCamelCase_    :str=20_48		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :Union[str, Any]=0.0		, lowerCamelCase_    :List[str]=0.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :int="gelu"		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :int=0.0		, lowerCamelCase_    :Tuple=0.0		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :Union[str, Any]=1		, lowerCamelCase_    :Dict=False		, lowerCamelCase_    :Optional[int]=0		, lowerCamelCase_    :List[Any]=1		, lowerCamelCase_    :Any=2		, lowerCamelCase_    :Optional[Any]=2		, **lowerCamelCase_    :Dict		, )							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= d_model
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     Tuple				= encoder_layers
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_attention_heads
			SCREAMING_SNAKE_CASE       :     Any				= decoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     str				= decoder_layers
			SCREAMING_SNAKE_CASE       :     str				= decoder_attention_heads
			SCREAMING_SNAKE_CASE       :     List[Any]				= dropout
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_dropout
			SCREAMING_SNAKE_CASE       :     Any				= activation_dropout
			SCREAMING_SNAKE_CASE       :     List[str]				= activation_function
			SCREAMING_SNAKE_CASE       :     Optional[int]				= init_std
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_layerdrop
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= decoder_layerdrop
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_cache
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

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







			'''simple docstring'''




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

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

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







			'''simple docstring'''




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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

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

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

					SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_					)]		, dim=1					)

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

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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

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

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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= compute_effective_axis_dimension(
			    lowerCamelCase_		, 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
			SCREAMING_SNAKE_CASE       :     int				= tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_					)

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





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







			'''simple docstring'''




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

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

			return common_inputs







	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Dict					)							->			List[Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= super()._flatten_past_key_values_(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= super(lowerCamelCase_		, self					)._flatten_past_key_values_(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





