

import warnings
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


a_      =     logging.get_logger(__name__)

a_      =     {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bart'
			lowerCamelCase__							     =['past_key_values']
			lowerCamelCase__							     ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(      self				:			str   ,   a				:			Optional[int]=5_0265   ,   a				:			Any=1024   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=4096   ,   a				:			Any=16   ,   a				:			Optional[int]=12   ,   a				:			Any=4096   ,   a				:			List[Any]=16   ,   a				:			Union[str, Any]=0.0   ,   a				:			List[str]=0.0   ,   a				:			List[Any]="gelu"   ,   a				:			Dict=1024   ,   a				:			Union[str, Any]=0.1   ,   a				:			Any=0.0   ,   a				:			Union[str, Any]=0.0   ,   a				:			Union[str, Any]=0.02   ,   a				:			Optional[int]=0.0   ,   a				:			Tuple=False   ,   a				:			Dict=True   ,   a				:			Optional[int]=3   ,   a				:			Union[str, Any]=1   ,   a				:			str=0   ,   a				:			Dict=2   ,   a				:			Any=True   ,   a				:			str=2   ,   a				:			Optional[Any]=2   ,   **a				:			List[str]   ,   )				->							List[Any]:

				"""simple docstring"""

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

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

				# ensure backward compatibility for BART CNN models
				if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"   ,   a						):
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.bos_token_id
					warnings.warn(
					    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
					    "The config can simply be saved and uploaded again to be fixed."						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

				"""simple docstring"""

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

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

					if self.use_past:
						self.fill_with_past_key_values_(a   ,   direction="inputs"						)
				elif self.task == "causal-lm":
					# TODO: figure this case out.
					SCREAMING_SNAKE_CASE :   str					  =							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 :   Any					  =							self.num_layers
						for i in range(a						):
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE :   int					  =							{0: "batch", 2: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			List[str]						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							super().outputs
				else:
					SCREAMING_SNAKE_CASE :   str					  =							super(a   ,   self						).outputs
					if self.use_past:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self.num_layers
						for i in range(a						):
							SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE :   Any					  =							{0: "batch", 2: "past_sequence + sequence"}
				return common_outputs
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a   ,   a   ,   a   ,   a   ,   a						)

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

				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 :   Union[str, Any]					  =							common_inputs["input_ids"].shape
					SCREAMING_SNAKE_CASE :   Tuple					  =							common_inputs["decoder_input_ids"].shape[1]
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.num_attention_heads
					SCREAMING_SNAKE_CASE :   int					  =							(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							decoder_seq_length + 3
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(a   ,   a						)]   ,   dim=1						)

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

					for _ in range(a						):
						common_inputs["past_key_values"].append(
						    (
						        torch.zeros(a						),
						        torch.zeros(a						),
						        torch.zeros(a						),
						        torch.zeros(a						),
						    )						)
					# TODO: test this.
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							encoder_shape if remaining_side_name == "encoder" else decoder_shape
					for _ in range(a   ,   a						):
						common_inputs["past_key_values"].append((torch.zeros(a						), torch.zeros(a						))						)
				return common_inputs
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a   ,   a   ,   a   ,   a   ,   a						)

				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 :   Union[str, Any]					  =							common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE :   Any					  =							seqlen + 2
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.num_layers
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							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 :   Union[str, Any]					  =							common_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.cat(
					    [common_inputs["attention_mask"], torch.ones(a   ,   a   ,   dtype=a						)]   ,   dim=1						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							[
					    (torch.zeros(a						), torch.zeros(a						)) for _ in range(a						)
					]
				return common_inputs
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							compute_effective_axis_dimension(
				    a   ,   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(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							compute_effective_axis_dimension(
				    a   ,   fixed_dimension=OnnxConfig.default_fixed_sequence   ,   num_token_to_add=a						)

				# Generate dummy inputs according to compute batch and sequence
				SCREAMING_SNAKE_CASE :   str					  =							[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							dict(tokenizer(a   ,   return_tensors=a						)						)
				return common_inputs
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

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

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

				return common_inputs






			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Tuple   ,   a				:			Tuple   ,   a				:			int   ,   a				:			Tuple						)				->							Tuple:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							super()._flatten_past_key_values_(a   ,   a   ,   a   ,   a						)
				else:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							super(a   ,   self						)._flatten_past_key_values_(
					    a   ,   a   ,   a   ,   a						)


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							UNetaDModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2