
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    a_ (			_a					,			unittest.TestCase    ):
			a	:			List[str]					   =							KandinskyInpaintPipeline
			a	:			Dict					   =							['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
			a	:			Optional[int]					   =							[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			    '''mask_image''',
			]
			a	:			Optional[Any]					   =							[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			a	:			str					   =							False






			@property
			def    UpperCamelCase_				(      self  ):
									return 32






			@property
			def    UpperCamelCase_				(      self  ):
									return 32






			@property
			def    UpperCamelCase_				(      self  ):
									return self.time_input_dim






			@property
			def    UpperCamelCase_				(      self  ):
									return self.time_input_dim * 4






			@property
			def    UpperCamelCase_				(      self  ):
									return 100






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
									return tokenizer






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )
									_lowercase					       =					MCLIPConfig(
									    numDims=self.cross_attention_dim				,			transformerDimensions=self.text_embedder_hidden_size				,			hidden_size=self.text_embedder_hidden_size				,			intermediate_size=37				,			num_attention_heads=4				,			num_hidden_layers=5				,			vocab_size=1_005				,			)

									_lowercase					       =					MultilingualCLIP(__UpperCamelCase  )
									_lowercase					       =					text_encoder.eval()

									return text_encoder






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )

									_lowercase					       =					{
									    """in_channels""": 9,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """text_image""",
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """encoder_hid_dim""": self.text_embedder_hidden_size,
									    """encoder_hid_dim_type""": """text_image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									_lowercase					       =					UNetaDConditionModel(**__UpperCamelCase  )
									return model






			@property
			def    UpperCamelCase_				(      self  ):
									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )
									_lowercase					       =					VQModel(**self.dummy_movq_kwargs  )
									return model






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.dummy_text_encoder
									_lowercase					       =					self.dummy_tokenizer
									_lowercase					       =					self.dummy_unet
									_lowercase					       =					self.dummy_movq

									_lowercase					       =					DDIMScheduler(
									    num_train_timesteps=1_000				,			beta_schedule="""linear"""				,			beta_start=0.0_00_85				,			beta_end=0.0_12				,			clip_sample=__UpperCamelCase				,			set_alpha_to_one=__UpperCamelCase				,			steps_offset=1				,			prediction_type="""epsilon"""				,			thresholding=__UpperCamelCase				,			)

									_lowercase					       =					{
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase=0  ):
									_lowercase					       =					floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(__UpperCamelCase  )  ).to(__UpperCamelCase  )
									_lowercase					       =					floats_tensor((1, self.cross_attention_dim)				,			rng=random.Random(seed + 1  )  ).to(__UpperCamelCase  )
									# create init_image
									_lowercase					       =					floats_tensor((1, 3, 64, 64)				,			rng=random.Random(__UpperCamelCase  )  ).to(__UpperCamelCase  )
									_lowercase					       =					image.cpu().permute(0				,			2				,			3				,			1  )[0]
									_lowercase					       =					Image.fromarray(np.uinta(__UpperCamelCase  )  ).convert("""RGB"""  ).resize((256, 256)  )
									# create mask
									_lowercase					       =					np.ones((64, 64)				,			dtype=np.floataa  )
									_lowercase					       =					0

									if str(__UpperCamelCase  ).startswith("""mps"""  ):
															_lowercase					       =					torch.manual_seed(__UpperCamelCase  )
									else:
															_lowercase					       =					torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
									_lowercase					       =					{
									    """prompt""": """horse""",
									    """image""": init_image,
									    """mask_image""": mask,
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 4.0,
									    """output_type""": """np""",
									}
									return inputs






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""cpu"""

									_lowercase					       =					self.get_dummy_components()

									_lowercase					       =					self.pipeline_class(**__UpperCamelCase  )
									_lowercase					       =					pipe.to(__UpperCamelCase  )

									pipe.set_progress_bar_config(disable=__UpperCamelCase  )

									_lowercase					       =					pipe(**self.get_dummy_inputs(__UpperCamelCase  )  )
									_lowercase					       =					output.images

									_lowercase					       =					pipe(
									    **self.get_dummy_inputs(__UpperCamelCase  )				,			return_dict=__UpperCamelCase				,			)[0]

									_lowercase					       =					image[0, -3:, -3:, -1]
									_lowercase					       =					image_from_tuple[0, -3:, -3:, -1]

									print(f"""image.shape {image.shape}"""  )

									assert image.shape == (1, 64, 64, 3)

									_lowercase					       =					np.array(
									    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]  )

									assert (
									    np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
									), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
									), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






			def    UpperCamelCase_				(      self  ):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3  )







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






			def    UpperCamelCase_				(      self  ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""  )

									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
									_lowercase					       =					np.ones((768, 768)				,			dtype=np.floataa  )
									_lowercase					       =					0

									_lowercase					       =					"""a hat"""

									_lowercase					       =					KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""				,			torch_dtype=torch.floataa  )
									pipe_prior.to(__UpperCamelCase  )

									_lowercase					       =					KandinskyInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-inpaint"""				,			torch_dtype=torch.floataa  )
									_lowercase					       =					pipeline.to(__UpperCamelCase  )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase  )

									_lowercase					       =					torch.Generator(device="""cpu"""  ).manual_seed(0  )
									_lowercase       ,						_lowercase					       =					pipe_prior(
									    __UpperCamelCase				,			generator=__UpperCamelCase				,			num_inference_steps=5				,			negative_prompt=""""""				,			).to_tuple()

									_lowercase					       =					pipeline(
									    __UpperCamelCase				,			image=__UpperCamelCase				,			mask_image=__UpperCamelCase				,			image_embeds=__UpperCamelCase				,			negative_image_embeds=__UpperCamelCase				,			generator=__UpperCamelCase				,			num_inference_steps=100				,			height=768				,			width=768				,			output_type="""np"""				,			)

									_lowercase					       =					output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(__UpperCamelCase				,			__UpperCamelCase  )

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


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''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()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[Any]								=     [
    ('''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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

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

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


if __name__ == "__main__":
		A		: List[Any]								=     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'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)