import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCAmelCase__	:  Optional[int]						=  logging.get_logger(__name__)  # pylint: disable=invalid-name
def 					UpperCamelCase__		(							self     ,					A__     ,					A__     ,					A__     ,					A__       )  ->   Optional[Any]:
				snake_case__   :      List[str]   				= image.to(device=A__       )
				snake_case__   :      Optional[int]   				= image  # take image as image_embeddings
				snake_case__   :      Any   				= image_embeddings.unsqueeze(1       )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				snake_case__				,    snake_case__				,    snake_case__   :      Optional[int]   				= image_embeddings.shape
				snake_case__   :      Optional[Any]   				= image_embeddings.repeat(1     ,					A__     ,					1       )
				snake_case__   :      str   				= image_embeddings.view(bs_embed * num_images_per_prompt     ,					A__     ,					-1       )

				if do_classifier_free_guidance:
								snake_case__   :      int   				= torch.zeros_like(A__       )

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								snake_case__   :      Any   				= torch.cat([uncond_embeddings, image_embeddings]       )

				return image_embeddings





class 					__snake_case    (     _lowerCamelCase      ):




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			)	->    List[Any]:



									'''simple docstring'''







									super().__init__()

									snake_case__   :      Optional[int]   				= {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									snake_case__   :      Tuple   				= decoder_pipe_kwargs.get('torch_dtype'       ,			__UpperCamelCase						) or prior.dtype

									snake_case__   :      List[str]   				= StableDiffusionImageVariationPipeline.from_pretrained(
									    'lambdalabs/sd-image-variations-diffusers'       ,			**__UpperCamelCase						)

									# replace `_encode_image` method
									snake_case__   :      Optional[int]   				= types.MethodType(_encode_image       ,			self.decoder_pipe						)

									self.register_modules(
									    prior=__UpperCamelCase       ,			tokenizer=__UpperCamelCase       ,			text_encoder=__UpperCamelCase       ,			prior_scheduler=__UpperCamelCase       ,			)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			)	->    Optional[Any]:



									'''simple docstring'''







									if text_model_output is None:
													snake_case__   :      Tuple   				= len(__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else 1
													# get prompt text embeddings
													snake_case__   :      str   				= self.tokenizer(
													    __UpperCamelCase       ,			padding='max_length'       ,			max_length=self.tokenizer.model_max_length       ,			return_tensors='pt'       ,			)
													snake_case__   :      Any   				= text_inputs.input_ids
													snake_case__   :      Tuple   				= text_inputs.attention_mask.bool().to(__UpperCamelCase						)

													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	snake_case__   :      Optional[int]   				= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
																	logger.warning(
																	    'The following part of your input was truncated because CLIP can only handle sequences up to'
																	    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""						)
																	snake_case__   :      List[str]   				= text_input_ids[:, : self.tokenizer.model_max_length]

													snake_case__   :      Optional[int]   				= self.text_encoder(text_input_ids.to(__UpperCamelCase						)						)

													snake_case__   :      List[str]   				= text_encoder_output.text_embeds
													snake_case__   :      Tuple   				= text_encoder_output.last_hidden_state

									else:
													snake_case__   :      Dict   				= text_model_output[0].shape[0]
													snake_case__				,    snake_case__   :      Optional[int]   				= text_model_output[0], text_model_output[1]
													snake_case__   :      Union[str, Any]   				= text_attention_mask

									snake_case__   :      List[Any]   				= text_embeddings.repeat_interleave(__UpperCamelCase       ,			dim=0						)
									snake_case__   :      Dict   				= text_encoder_hidden_states.repeat_interleave(__UpperCamelCase       ,			dim=0						)
									snake_case__   :      Union[str, Any]   				= text_mask.repeat_interleave(__UpperCamelCase       ,			dim=0						)

									if do_classifier_free_guidance:
													snake_case__   :      int   				= [''] * batch_size

													snake_case__   :      Dict   				= self.tokenizer(
													    __UpperCamelCase       ,			padding='max_length'       ,			max_length=self.tokenizer.model_max_length       ,			truncation=__UpperCamelCase       ,			return_tensors='pt'       ,			)
													snake_case__   :      Tuple   				= uncond_input.attention_mask.bool().to(__UpperCamelCase						)
													snake_case__   :      Any   				= self.text_encoder(uncond_input.input_ids.to(__UpperCamelCase						)						)

													snake_case__   :      int   				= uncond_embeddings_text_encoder_output.text_embeds
													snake_case__   :      Any   				= uncond_embeddings_text_encoder_output.last_hidden_state

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

													snake_case__   :      Union[str, Any]   				= uncond_embeddings.shape[1]
													snake_case__   :      List[Any]   				= uncond_embeddings.repeat(1       ,			__UpperCamelCase						)
													snake_case__   :      Any   				= uncond_embeddings.view(batch_size * num_images_per_prompt       ,			__UpperCamelCase						)

													snake_case__   :      Dict   				= uncond_text_encoder_hidden_states.shape[1]
													snake_case__   :      Any   				= uncond_text_encoder_hidden_states.repeat(1       ,			__UpperCamelCase       ,			1						)
													snake_case__   :      str   				= uncond_text_encoder_hidden_states.view(
													    batch_size * num_images_per_prompt       ,			__UpperCamelCase       ,			-1						)
													snake_case__   :      Dict   				= uncond_text_mask.repeat_interleave(__UpperCamelCase       ,			dim=0						)

													# done duplicates

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													snake_case__   :      Any   				= torch.cat([uncond_embeddings, text_embeddings]						)
													snake_case__   :      Optional[Any]   				= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

													snake_case__   :      List[str]   				= torch.cat([uncond_text_mask, text_mask]						)

									return text_embeddings, text_encoder_hidden_states, text_mask




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



									'''simple docstring'''







									if self.device != torch.device('meta'						) or not hasattr(self.prior       ,			'_hf_hook'						):
													return self.device
									for module in self.prior.modules():
													if (
													    hasattr(__UpperCamelCase       ,			'_hf_hook'						)
													    and hasattr(module._hf_hook       ,			'execution_device'						)
													    and module._hf_hook.execution_device is not None
													):
																	return torch.device(module._hf_hook.execution_device						)
									return self.device




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if latents is None:
													snake_case__   :      List[str]   				= randn_tensor(__UpperCamelCase       ,			generator=__UpperCamelCase       ,			device=__UpperCamelCase       ,			dtype=__UpperCamelCase						)
									else:
													if latents.shape != shape:
																	raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""						)
													snake_case__   :      Tuple   				= latents.to(__UpperCamelCase						)

									snake_case__   :      Tuple   				= latents * scheduler.init_noise_sigma
									return latents




					def      __a     (  self       ,			__UpperCamelCase = None						)	->    Optional[Any]:



									'''simple docstring'''







									self.decoder_pipe.to(__UpperCamelCase						)
									super().to(__UpperCamelCase						)




					@torch.no_grad()
					def __call__(  self       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = 1       ,			__UpperCamelCase = 25       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = None       ,			__UpperCamelCase = 4.0       ,			__UpperCamelCase = 8.0       ,			__UpperCamelCase = 50       ,			__UpperCamelCase = 1       ,			__UpperCamelCase = 0.0       ,			__UpperCamelCase = "pil"       ,			__UpperCamelCase = True       ,			)	->    int:



									'''simple docstring'''







									if prompt is not None:
													if isinstance(__UpperCamelCase       ,			__UpperCamelCase						):
																	snake_case__   :      Optional[int]   				= 1
													elif isinstance(__UpperCamelCase       ,			__UpperCamelCase						):
																	snake_case__   :      List[Any]   				= len(__UpperCamelCase						)
													else:
																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase						)}"""						)
									else:
													snake_case__   :      Any   				= text_model_output[0].shape[0]

									snake_case__   :      List[str]   				= self._execution_device

									snake_case__   :      Optional[Any]   				= batch_size * num_images_per_prompt

									snake_case__   :      Dict   				= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									snake_case__				,    snake_case__				,    snake_case__   :      str   				= self._encode_prompt(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									# prior

									self.prior_scheduler.set_timesteps(__UpperCamelCase       ,			device=__UpperCamelCase						)
									snake_case__   :      Optional[Any]   				= self.prior_scheduler.timesteps

									snake_case__   :      Union[str, Any]   				= self.prior.config.embedding_dim

									snake_case__   :      List[Any]   				= self.prepare_latents(
									    (batch_size, embedding_dim)       ,			text_embeddings.dtype       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			self.prior_scheduler       ,			)

									for i, t in enumerate(self.progress_bar(__UpperCamelCase						)						):
													# expand the latents if we are doing classifier free guidance
													snake_case__   :      Any   				= torch.cat([prior_latents] * 2						) if do_classifier_free_guidance else prior_latents

													snake_case__   :      Union[str, Any]   				= self.prior(
													    __UpperCamelCase       ,			timestep=__UpperCamelCase       ,			proj_embedding=__UpperCamelCase       ,			encoder_hidden_states=__UpperCamelCase       ,			attention_mask=__UpperCamelCase       ,			).predicted_image_embedding

													if do_classifier_free_guidance:
																	snake_case__				,    snake_case__   :      Optional[int]   				= predicted_image_embedding.chunk(2						)
																	snake_case__   :      Dict   				= predicted_image_embedding_uncond + prior_guidance_scale * (
																	    predicted_image_embedding_text - predicted_image_embedding_uncond
																	)

													if i + 1 == prior_timesteps_tensor.shape[0]:
																	snake_case__   :      Union[str, Any]   				= None
													else:
																	snake_case__   :      Any   				= prior_timesteps_tensor[i + 1]

													snake_case__   :      Optional[int]   				= self.prior_scheduler.step(
													    __UpperCamelCase       ,			timestep=__UpperCamelCase       ,			sample=__UpperCamelCase       ,			generator=__UpperCamelCase       ,			prev_timestep=__UpperCamelCase       ,			).prev_sample

									snake_case__   :      Union[str, Any]   				= self.prior.post_process_latents(__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= prior_latents

									snake_case__   :      Optional[int]   				= self.decoder_pipe(
									    image=__UpperCamelCase       ,			height=__UpperCamelCase       ,			width=__UpperCamelCase       ,			num_inference_steps=__UpperCamelCase       ,			guidance_scale=__UpperCamelCase       ,			generator=__UpperCamelCase       ,			output_type=__UpperCamelCase       ,			return_dict=__UpperCamelCase       ,			num_images_per_prompt=__UpperCamelCase       ,			eta=__UpperCamelCase       ,			)
									return output



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


lowerCAmelCase__	:  List[Any]						=  logging.get_logger(__name__)

lowerCAmelCase__	:  str						=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """bigbird_pegasus"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {
					    """num_attention_heads""": """encoder_attention_heads""",
					    """hidden_size""": """d_model""",
					    """attention_probs_dropout_prob""": """attention_dropout""",
					}




					def __init__(  self       ,			__UpperCamelCase=96103       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="gelu_new"       ,			__UpperCamelCase=1024       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=1       ,			__UpperCamelCase="block_sparse"       ,			__UpperCamelCase=64       ,			__UpperCamelCase=3       ,			__UpperCamelCase=False       ,			**__UpperCamelCase       ,			)	->    Tuple:



									'''simple docstring'''







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

									# extra config
									snake_case__   :      Optional[Any]   				= attention_type
									snake_case__   :      Optional[int]   				= block_size
									snake_case__   :      Optional[Any]   				= num_random_blocks
									snake_case__   :      List[str]   				= use_bias

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





class 					__snake_case    (     _lowerCamelCase      ):




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



									'''simple docstring'''







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

													if self.use_past:
																	snake_case__   :      Optional[int]   				= {0: 'batch'}
																	snake_case__   :      str   				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	snake_case__   :      Any   				= {0: 'batch', 1: 'decoder_sequence'}
																	snake_case__   :      List[str]   				= {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.
													snake_case__   :      Any   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)
													if self.use_past:
																	snake_case__				,    snake_case__   :      Optional[Any]   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      str   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      Optional[int]   				= {0: 'batch', 2: 'past_sequence + sequence'}
									else:
													snake_case__   :      Union[str, 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      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									# Generate decoder inputs
									snake_case__   :      Optional[Any]   				= seq_length if not self.use_past else 1
									snake_case__   :      int   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      str   				= {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case__   :      Any   				= 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
													snake_case__				,    snake_case__   :      int   				= common_inputs['input_ids'].shape
													snake_case__   :      int   				= common_inputs['decoder_input_ids'].shape[1]
													snake_case__				,    snake_case__   :      List[Any]   				= self.num_attention_heads
													snake_case__   :      Any   				= (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													snake_case__   :      Optional[int]   				= decoder_seq_length + 3
													snake_case__   :      Union[str, Any]   				= (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													snake_case__   :      Union[str, Any]   				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase						)]       ,			dim=1						)

													snake_case__   :      Optional[Any]   				= []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													snake_case__				,    snake_case__   :      str   				= self.num_layers
													snake_case__   :      str   				= min(__UpperCamelCase       ,			__UpperCamelCase						)
													snake_case__   :      Optional[int]   				= max(__UpperCamelCase       ,			__UpperCamelCase						) - min_num_layers
													snake_case__   :      int   				= '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.
													snake_case__   :      Dict   				= 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= 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
													snake_case__				,    snake_case__   :      Optional[int]   				= common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													snake_case__   :      str   				= seqlen + 2
													snake_case__				,    snake_case__   :      List[str]   				= self.num_layers
													snake_case__				,    snake_case__   :      str   				= self.num_attention_heads
													snake_case__   :      Tuple   				= (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 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
									snake_case__   :      str   				= tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
									snake_case__   :      List[str]   				= 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
									snake_case__   :      Optional[Any]   				= [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
									snake_case__   :      Optional[Any]   				= dict(tokenizer(__UpperCamelCase       ,			return_tensors=__UpperCamelCase						)						)
									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      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":
													snake_case__   :      Union[str, Any]   				= self._generate_dummy_inputs_for_causal_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)
									else:
													snake_case__   :      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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Optional[int]   				= super()._flatten_past_key_values_(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									else:
													snake_case__   :      Optional[Any]   				= super(__UpperCamelCase       ,			self						)._flatten_past_key_values_(
													    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)


