





"""simple docstring"""
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


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

_snake_case     :		Optional[Any]       						= {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					_UpperCAmelCase   (	_SCREAMING_SNAKE_CASE ):
					UpperCamelCase      =							"marian"
					UpperCamelCase      =							["past_key_values"]
					UpperCamelCase      =							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:List[str]=5_81_01     ,       __UpperCamelCase			:Tuple=None     ,       __UpperCamelCase			:str=10_24     ,       __UpperCamelCase			:str=12     ,       __UpperCamelCase			:Optional[Any]=40_96     ,       __UpperCamelCase			:Union[str, Any]=16     ,       __UpperCamelCase			:int=12     ,       __UpperCamelCase			:List[Any]=40_96     ,       __UpperCamelCase			:Dict=16     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:List[str]=0.0     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:Any=True     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Union[str, Any]=10_24     ,       __UpperCamelCase			:List[str]=0.1     ,       __UpperCamelCase			:Any=0.0     ,       __UpperCamelCase			:Optional[Any]=0.0     ,       __UpperCamelCase			:Any=0.02     ,       __UpperCamelCase			:Optional[int]=5_81_00     ,       __UpperCamelCase			:int=False     ,       __UpperCamelCase			:Dict=5_81_00     ,       __UpperCamelCase			:Tuple=0     ,       __UpperCamelCase			:Dict=0     ,       __UpperCamelCase			:Optional[Any]=True     ,       **__UpperCamelCase			:List[str]     ,       ):
												A		=  vocab_size
												A		=  decoder_vocab_size or vocab_size
												A		=  max_position_embeddings
												A		=  d_model
												A		=  encoder_ffn_dim
												A		=  encoder_layers
												A		=  encoder_attention_heads
												A		=  decoder_ffn_dim
												A		=  decoder_layers
												A		=  decoder_attention_heads
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  activation_function
												A		=  init_std
												A		=  encoder_layerdrop
												A		=  decoder_layerdrop
												A		=  use_cache
												A		=  encoder_layers
												A		=  scale_embedding  # scale factor will be sqrt(d_model) if True
												A		=  share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=A_     ,       eos_token_id=A_     ,       is_encoder_decoder=A_     ,       decoder_start_token_id=A_     ,       forced_eos_token_id=A_     ,       **A_     ,       )







class 					_UpperCAmelCase   (	_SCREAMING_SNAKE_CASE ):


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)

																			if self.use_past:
																										A		=  {0: "batch"}
																										A		=  {0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																										A		=  {0: "batch", 1: "decoder_sequence"}
																										A		=  {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.
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(A_		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												else:
																			A		=  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
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 	lowerCamelCase       (					self			:Tuple		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super().outputs
												else:
																			A		=  super(A_     ,       self		).outputs
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(A_		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												return common_outputs


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Dict = -1     ,       __UpperCamelCase			:Tuple = -1     ,       __UpperCamelCase			:List[Any] = False     ,       __UpperCamelCase			:Dict = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    A_     ,       A_     ,       A_     ,       A_     ,       A_		)

												# Generate decoder inputs
												A		=  seq_length if not self.use_past else 1
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    A_     ,       A_     ,       A_     ,       A_     ,       A_		)
												A		=  {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												A		=  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
																			A,	A		=  common_inputs["input_ids"].shape
																			A		=  common_inputs["decoder_input_ids"].shape[1]
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    encoder_seq_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)
																			A		=  decoder_seq_length + 3
																			A		=  (
																			    batch,
																			    num_decoder_attention_heads,
																			    decoder_past_length,
																			    self._config.hidden_size // num_decoder_attention_heads,
																			)

																			A		=  torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(A_     ,       A_		)]     ,       dim=1		)

																			A		=  []
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			A,	A		=  self.num_layers
																			A		=  min(A_     ,       A_		)
																			A		=  max(A_     ,       A_		) - min_num_layers
																			A		=  "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.
																			A		=  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 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:str = -1     ,       __UpperCamelCase			:List[Any] = False     ,       __UpperCamelCase			:Tuple = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    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
																			A,	A		=  common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			A		=  seqlen + 2
																			A,	A		=  self.num_layers
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

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


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:Optional[int] = -1     ,       __UpperCamelCase			:Any = -1     ,       __UpperCamelCase			:Optional[Any] = False     ,       __UpperCamelCase			:int = None     ,       ):
												A		=  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
												A		=  tokenizer.num_special_tokens_to_add(A_		)
												A		=  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
												A		=  [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
												A		=  dict(tokenizer(A_     ,       return_tensors=A_		)		)
												return common_inputs


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Union[str, Any] = -1     ,       __UpperCamelCase			:Dict = -1     ,       __UpperCamelCase			:List[str] = False     ,       __UpperCamelCase			:Optional[int] = None     ,       ):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    A_     ,       batch_size=A_     ,       seq_length=A_     ,       is_pair=A_     ,       framework=A_		)

												else:
																			A		=  self._generate_dummy_inputs_for_causal_lm(
																			    A_     ,       batch_size=A_     ,       seq_length=A_     ,       is_pair=A_     ,       framework=A_		)

												return common_inputs


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:Optional[int]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super()._flatten_past_key_values_(A_     ,       A_     ,       A_     ,       A_		)
												else:
																			A		=  super(A_     ,       self		)._flatten_past_key_values_(
																			    A_     ,       A_     ,       A_     ,       A_		)


					@property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return 1e-4




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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