"""simple docstring"""
import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE__      						=  ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
SCREAMING_SNAKE_CASE__      						=  {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
					raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  " Hello world! cécé herlolip"

SCREAMING_SNAKE_CASE__      						=  [
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple					)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			[
						    'encoder.version',
						    'decoder.version',
						    'model.encoder.version',
						    'model.decoder.version',
						    '_float_tensor',
						]
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,			__lowerCAmelCase					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[str] ,			_UpperCamelCase       :				Dict ,			_UpperCamelCase       :				Any					)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			dct.pop(__lowerCAmelCase					)
						snake_case								=			val





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Any:





						"""simple docstring"""






						snake_case								=			torch.load(__lowerCAmelCase ,			map_location='cpu'					)
						snake_case								=			torch.hub.load('pytorch/fairseq' ,			'bart.large.cnn'					).eval()
						hub_interface.model.load_state_dict(sd['model']					)
						return hub_interface





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[str]					)					->				List[str]:





						"""simple docstring"""






						snake_case					,snake_case								=			emb.weight.shape
						snake_case								=			nn.Linear(__lowerCAmelCase ,			__lowerCAmelCase ,			bias=__lowerCAmelCase					)
						snake_case								=			emb.weight.data
						return lin_layer





@torch.no_grad()
def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Dict=None					)					->				str:





						"""simple docstring"""






						if not os.path.exists(__lowerCAmelCase					):
												snake_case								=			torch.hub.load('pytorch/fairseq' ,			__lowerCAmelCase					).eval()
						else:
												snake_case								=			load_xsum_checkpoint(__lowerCAmelCase					)

						bart.model.upgrade_state_dict(bart.model.state_dict()					)
						if hf_checkpoint_name is None:
												snake_case								=			checkpoint_path.replace('.' ,			'-'					)
						snake_case								=			BartConfig.from_pretrained(__lowerCAmelCase					)
						snake_case								=			bart.encode(__lowerCAmelCase					).unsqueeze(0					)
						snake_case								=			BartTokenizer.from_pretrained(__lowerCAmelCase					).encode(__lowerCAmelCase ,			return_tensors='pt'					).unsqueeze(0					)
						if not torch.eq(__lowerCAmelCase ,			__lowerCAmelCase					).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""					)

						if checkpoint_path == "bart.large.mnli":
												snake_case								=			bart.state_dict()
												remove_ignore_keys_(__lowerCAmelCase					)
												snake_case								=			state_dict['model.decoder.embed_tokens.weight']
												for src, dest in mnli_rename_keys:
																		rename_key(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase					)
												snake_case								=			BartForSequenceClassification(__lowerCAmelCase					).eval()
												model.load_state_dict(__lowerCAmelCase					)
												snake_case								=			bart.predict('mnli' ,			__lowerCAmelCase ,			return_logits=__lowerCAmelCase					)
												snake_case								=			model(__lowerCAmelCase					)[0]  # logits
						else:  # no classification heads to worry about
												snake_case								=			bart.model.state_dict()
												remove_ignore_keys_(__lowerCAmelCase					)
												snake_case								=			state_dict['decoder.embed_tokens.weight']
												snake_case								=			bart.extract_features(__lowerCAmelCase					)
												if hf_checkpoint_name == "facebook/bart-large":
																		snake_case								=			BartModel(__lowerCAmelCase					).eval()
																		model.load_state_dict(__lowerCAmelCase					)
																		snake_case								=			model(__lowerCAmelCase					).model[0]
												else:
																		snake_case								=			BartForConditionalGeneration(__lowerCAmelCase					).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(__lowerCAmelCase					)
																		if hasattr(__lowerCAmelCase ,			'lm_head'					):
																								snake_case								=			make_linear_from_emb(model.model.shared					)
																		snake_case								=			model.model(__lowerCAmelCase					)[0]

												# Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""					)
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'					)
						Path(__lowerCAmelCase					).mkdir(exist_ok=__lowerCAmelCase					)
						model.save_pretrained(__lowerCAmelCase					)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      						=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
					)
					parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument(
					    "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
					)
					SCREAMING_SNAKE_CASE__      						=  parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







"""simple docstring"""
SCREAMING_SNAKE_CASE__      						=  "0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
					if not is_onnx_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_onnx_objects import *  # noqa F403
else:
					from .pipelines import OnnxRuntimeModel

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_pt_objects import *  # noqa F403
else:
					from .models import (
					    AutoencoderKL,
					    ControlNetModel,
					    ModelMixin,
					    PriorTransformer,
					    TaFilmDecoder,
					    TransformeraDModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    VQModel,
					)
					from .optimization import (
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					    get_scheduler,
					)
					from .pipelines import (
					    AudioPipelineOutput,
					    ConsistencyModelPipeline,
					    DanceDiffusionPipeline,
					    DDIMPipeline,
					    DDPMPipeline,
					    DiffusionPipeline,
					    DiTPipeline,
					    ImagePipelineOutput,
					    KarrasVePipeline,
					    LDMPipeline,
					    LDMSuperResolutionPipeline,
					    PNDMPipeline,
					    RePaintPipeline,
					    ScoreSdeVePipeline,
					)
					from .schedulers import (
					    CMStochasticIterativeScheduler,
					    DDIMInverseScheduler,
					    DDIMParallelScheduler,
					    DDIMScheduler,
					    DDPMParallelScheduler,
					    DDPMScheduler,
					    DEISMultistepScheduler,
					    DPMSolverMultistepInverseScheduler,
					    DPMSolverMultistepScheduler,
					    DPMSolverSinglestepScheduler,
					    EulerAncestralDiscreteScheduler,
					    EulerDiscreteScheduler,
					    HeunDiscreteScheduler,
					    IPNDMScheduler,
					    KarrasVeScheduler,
					    KDPMaAncestralDiscreteScheduler,
					    KDPMaDiscreteScheduler,
					    PNDMScheduler,
					    RePaintScheduler,
					    SchedulerMixin,
					    ScoreSdeVeScheduler,
					    UnCLIPScheduler,
					    UniPCMultistepScheduler,
					    VQDiffusionScheduler,
					)
					from .training_utils import EMAModel

try:
					if not (is_torch_available() and is_scipy_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
					from .schedulers import LMSDiscreteScheduler

try:
					if not (is_torch_available() and is_torchsde_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
					from .schedulers import DPMSolverSDEScheduler

try:
					if not (is_torch_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    AltDiffusionImgaImgPipeline,
					    AltDiffusionPipeline,
					    AudioLDMPipeline,
					    CycleDiffusionPipeline,
					    IFImgaImgPipeline,
					    IFImgaImgSuperResolutionPipeline,
					    IFInpaintingPipeline,
					    IFInpaintingSuperResolutionPipeline,
					    IFPipeline,
					    IFSuperResolutionPipeline,
					    ImageTextPipelineOutput,
					    KandinskyImgaImgPipeline,
					    KandinskyInpaintPipeline,
					    KandinskyPipeline,
					    KandinskyPriorPipeline,
					    KandinskyVaaControlnetImgaImgPipeline,
					    KandinskyVaaControlnetPipeline,
					    KandinskyVaaImgaImgPipeline,
					    KandinskyVaaInpaintPipeline,
					    KandinskyVaaPipeline,
					    KandinskyVaaPriorEmbaEmbPipeline,
					    KandinskyVaaPriorPipeline,
					    LDMTextToImagePipeline,
					    PaintByExamplePipeline,
					    SemanticStableDiffusionPipeline,
					    ShapEImgaImgPipeline,
					    ShapEPipeline,
					    StableDiffusionAttendAndExcitePipeline,
					    StableDiffusionControlNetImgaImgPipeline,
					    StableDiffusionControlNetInpaintPipeline,
					    StableDiffusionControlNetPipeline,
					    StableDiffusionDepthaImgPipeline,
					    StableDiffusionDiffEditPipeline,
					    StableDiffusionImageVariationPipeline,
					    StableDiffusionImgaImgPipeline,
					    StableDiffusionInpaintPipeline,
					    StableDiffusionInpaintPipelineLegacy,
					    StableDiffusionInstructPixaPixPipeline,
					    StableDiffusionLatentUpscalePipeline,
					    StableDiffusionLDMaDPipeline,
					    StableDiffusionModelEditingPipeline,
					    StableDiffusionPanoramaPipeline,
					    StableDiffusionParadigmsPipeline,
					    StableDiffusionPipeline,
					    StableDiffusionPipelineSafe,
					    StableDiffusionPixaPixZeroPipeline,
					    StableDiffusionSAGPipeline,
					    StableDiffusionUpscalePipeline,
					    StableUnCLIPImgaImgPipeline,
					    StableUnCLIPPipeline,
					    TextToVideoSDPipeline,
					    TextToVideoZeroPipeline,
					    UnCLIPImageVariationPipeline,
					    UnCLIPPipeline,
					    UniDiffuserModel,
					    UniDiffuserPipeline,
					    UniDiffuserTextDecoder,
					    VersatileDiffusionDualGuidedPipeline,
					    VersatileDiffusionImageVariationPipeline,
					    VersatileDiffusionPipeline,
					    VersatileDiffusionTextToImagePipeline,
					    VideoToVideoSDPipeline,
					    VQDiffusionPipeline,
					)

try:
					if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionKDiffusionPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
					from .pipelines import (
					    OnnxStableDiffusionImgaImgPipeline,
					    OnnxStableDiffusionInpaintPipeline,
					    OnnxStableDiffusionInpaintPipelineLegacy,
					    OnnxStableDiffusionPipeline,
					    OnnxStableDiffusionUpscalePipeline,
					    StableDiffusionOnnxPipeline,
					)

try:
					if not (is_torch_available() and is_librosa_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
					from .pipelines import AudioDiffusionPipeline, Mel

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .pipelines import SpectrogramDiffusionPipeline

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_objects import *  # noqa F403
else:
					from .models.controlnet_flax import FlaxControlNetModel
					from .models.modeling_flax_utils import FlaxModelMixin
					from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .models.vae_flax import FlaxAutoencoderKL
					from .pipelines import FlaxDiffusionPipeline
					from .schedulers import (
					    FlaxDDIMScheduler,
					    FlaxDDPMScheduler,
					    FlaxDPMSolverMultistepScheduler,
					    FlaxKarrasVeScheduler,
					    FlaxLMSDiscreteScheduler,
					    FlaxPNDMScheduler,
					    FlaxSchedulerMixin,
					    FlaxScoreSdeVeScheduler,
					)


try:
					if not (is_flax_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    FlaxStableDiffusionControlNetPipeline,
					    FlaxStableDiffusionImgaImgPipeline,
					    FlaxStableDiffusionInpaintPipeline,
					    FlaxStableDiffusionPipeline,
					)

try:
					if not (is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_note_seq_objects import *  # noqa F403
else:
					from .pipelines import MidiProcessor


