"""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


_A   					=    logging.get_logger(__name__)

_A   					=    {
    '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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


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



					def __init__(						self			:				Any   , _snake_case			:				str=50265   , _snake_case			:				int=512   , _snake_case			:				Tuple=8   , _snake_case			:				Optional[int]=2048   , _snake_case			:				List[str]=16   , _snake_case			:				Any=8   , _snake_case			:				Optional[int]=2048   , _snake_case			:				Optional[Any]=16   , _snake_case			:				Any=0.0   , _snake_case			:				Any=0.0   , _snake_case			:				Optional[int]=True   , _snake_case			:				List[Any]=True   , _snake_case			:				Optional[int]="gelu"   , _snake_case			:				Optional[int]=512   , _snake_case			:				Optional[Any]=0.1   , _snake_case			:				List[Any]=0.0   , _snake_case			:				str=0.0   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Any=1   , _snake_case			:				Optional[Any]=False   , _snake_case			:				Any=0   , _snake_case			:				List[str]=1   , _snake_case			:				Tuple=2   , _snake_case			:				Union[str, Any]=2   , **_snake_case			:				Any   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				d_model
									SCREAMING_SNAKE_CASE__		  =				encoder_ffn_dim
									SCREAMING_SNAKE_CASE__		  =				encoder_layers
									SCREAMING_SNAKE_CASE__		  =				encoder_attention_heads
									SCREAMING_SNAKE_CASE__		  =				decoder_ffn_dim
									SCREAMING_SNAKE_CASE__		  =				decoder_layers
									SCREAMING_SNAKE_CASE__		  =				decoder_attention_heads
									SCREAMING_SNAKE_CASE__		  =				dropout
									SCREAMING_SNAKE_CASE__		  =				attention_dropout
									SCREAMING_SNAKE_CASE__		  =				activation_dropout
									SCREAMING_SNAKE_CASE__		  =				activation_function
									SCREAMING_SNAKE_CASE__		  =				init_std
									SCREAMING_SNAKE_CASE__		  =				encoder_layerdrop
									SCREAMING_SNAKE_CASE__		  =				decoder_layerdrop
									SCREAMING_SNAKE_CASE__		  =				use_cache
									SCREAMING_SNAKE_CASE__		  =				encoder_layers
									SCREAMING_SNAKE_CASE__		  =				scale_embedding  # scale factor will be sqrt(d_model) if True

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



class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]		)

													if self.use_past:
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch"}
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "decoder_sequence"}
																	SCREAMING_SNAKE_CASE__		  =				{0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																	self.fill_with_past_key_values_(_snake_case   , direction="inputs"		)
									elif self.task == "causal-lm":
													# TODO: figure this case out.
													SCREAMING_SNAKE_CASE__		  =				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__		  =				self.num_layers
																	for i in range(_snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
									else:
													SCREAMING_SNAKE_CASE__		  =				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			:				Union[str, Any]		)							->       Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				super().outputs
									else:
													SCREAMING_SNAKE_CASE__		  =				super(_snake_case   , self		).outputs
													if self.use_past:
																	SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
																	for i in range(_snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
																					SCREAMING_SNAKE_CASE__		  =				{0: "batch", 2: "past_sequence + sequence"}
									return common_outputs



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)

									# Generate decoder inputs
									SCREAMING_SNAKE_CASE__		  =				seq_length if not self.use_past else 1
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									SCREAMING_SNAKE_CASE__		  =				dict(**_snake_case   , **_snake_case		)

									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__		  =				common_inputs["input_ids"].shape
													SCREAMING_SNAKE_CASE__		  =				common_inputs["decoder_input_ids"].shape[1]
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_attention_heads
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													SCREAMING_SNAKE_CASE__		  =				decoder_seq_length + 3
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(_snake_case   , _snake_case		)]   , dim=1		)

													SCREAMING_SNAKE_CASE__		  =				[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
													SCREAMING_SNAKE_CASE__		  =				min(_snake_case   , _snake_case		)
													SCREAMING_SNAKE_CASE__		  =				max(_snake_case   , _snake_case		) - min_num_layers
													SCREAMING_SNAKE_CASE__		  =				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case		)

									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__		  =				common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													SCREAMING_SNAKE_CASE__		  =				seqlen + 2
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_layers
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self.num_attention_heads
													SCREAMING_SNAKE_CASE__		  =				(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													SCREAMING_SNAKE_CASE__		  =				common_inputs["attention_mask"].dtype
													SCREAMING_SNAKE_CASE__		  =				torch.cat(
													    [common_inputs["attention_mask"], torch.ones(_snake_case   , _snake_case   , dtype=_snake_case		)]   , dim=1		)
													SCREAMING_SNAKE_CASE__		  =				[
													    (torch.zeros(_snake_case		), torch.zeros(_snake_case		)) for _ in range(_snake_case		)
													]
									return common_inputs



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE__		  =				compute_effective_axis_dimension(
									    _snake_case   , 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__		  =				tokenizer.num_special_tokens_to_add(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				compute_effective_axis_dimension(
									    _snake_case   , fixed_dimension=OnnxConfig.default_fixed_sequence   , num_token_to_add=_snake_case		)

									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE__		  =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE__		  =				dict(tokenizer(_snake_case   , return_tensors=_snake_case		)		)
									return common_inputs



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				PreTrainedTokenizer   , _snake_case			:				int = -1   , _snake_case			:				int = -1   , _snake_case			:				bool = False   , _snake_case			:				Optional[TensorType] = None   , )							->       Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)

									elif self.task == "causal-lm":
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_causal_lm(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _snake_case   , batch_size=_snake_case   , seq_length=_snake_case   , is_pair=_snake_case   , framework=_snake_case		)

									return common_inputs



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[int]		)							->       Union[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
													SCREAMING_SNAKE_CASE__		  =				super()._flatten_past_key_values_(_snake_case   , _snake_case   , _snake_case   , _snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				super(_snake_case   , self		)._flatten_past_key_values_(
													    _snake_case   , _snake_case   , _snake_case   , _snake_case		)





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_snake_case   , _snake_case		) and len(_snake_case		) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(_snake_case		)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				(image / 2 + 0.5).clamp(0   , 1		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		).numpy()
									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




