





'''simple docstring'''



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 							__lowercase    ( __lowercase	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			Optional[int]     =    KandinskyInpaintPipeline
	a      :			Optional[int]     =    ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a      :			Union[str, Any]     =    [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a      :			Optional[int]     =    [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a      :			Any     =    False


	@property
	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				return 32


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return 32


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return self.time_input_dim


	@property
	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				return self.time_input_dim * 4


	@property
	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				return 100


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''	)
				return tokenizer


	@property
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				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=1005       ,)

				__lowercase			  =							MultilingualCLIP(snake_case_	)
				__lowercase			  =							text_encoder.eval()

				return text_encoder


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				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(**snake_case_	)
				return model


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				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	)							->							List[Any]:







				'''simple docstring'''





				torch.manual_seed(0	)
				__lowercase			  =							VQModel(**self.dummy_movq_kwargs	)
				return model


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							self.dummy_text_encoder
				__lowercase			  =							self.dummy_tokenizer
				__lowercase			  =							self.dummy_unet
				__lowercase			  =							self.dummy_movq

				__lowercase			  =							DDIMScheduler(
				    num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0_0_8_5       ,beta_end=0.0_1_2       ,clip_sample=snake_case_       ,set_alpha_to_one=snake_case_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=snake_case_       ,)

				__lowercase			  =							{
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''movq''': movq,
				}

				return components


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=0	)							->							Dict:







				'''simple docstring'''





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

				if str(snake_case_	).startswith('''mps'''	):
							__lowercase			  =							torch.manual_seed(snake_case_	)
				else:
							__lowercase			  =							torch.Generator(device=snake_case_	).manual_seed(snake_case_	)
				__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	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							'''cpu'''

				__lowercase			  =							self.get_dummy_components()

				__lowercase			  =							self.pipeline_class(**snake_case_	)
				__lowercase			  =							pipe.to(snake_case_	)

				pipe.set_progress_bar_config(disable=snake_case_	)

				__lowercase			  =							pipe(**self.get_dummy_inputs(snake_case_	)	)
				__lowercase			  =							output.images

				__lowercase			  =							pipe(
				    **self.get_dummy_inputs(snake_case_	)       ,return_dict=snake_case_       ,)[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_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]	)

				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	)							->							List[str]:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__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(snake_case_	)

				__lowercase			  =							KandinskyInpaintPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa	)
				__lowercase			  =							pipeline.to(snake_case_	)
				pipeline.set_progress_bar_config(disable=snake_case_	)

				__lowercase			  =							torch.Generator(device='''cpu'''	).manual_seed(0	)
				__lowercase			  =							pipe_prior(
				    snake_case_       ,generator=snake_case_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

				__lowercase			  =							pipeline(
				    snake_case_       ,image=snake_case_       ,mask_image=snake_case_       ,image_embeds=snake_case_       ,negative_image_embeds=snake_case_       ,generator=snake_case_       ,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(snake_case_       ,snake_case_	)












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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							dct.pop(lowerCamelCase_			)
			__lowercase			  =							val




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any			):
			__lowercase			  =							torch.load(lowerCamelCase_						,    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 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			__lowercase  ,						__lowercase			  =							emb.weight.shape
			__lowercase			  =							nn.Linear(lowerCamelCase_						,    lowerCamelCase_						,    bias=lowerCamelCase_			)
			__lowercase			  =							emb.weight.data
			return lin_layer




@torch.no_grad()
def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[Any]=None			):
			if not os.path.exists(lowerCamelCase_			):
						__lowercase			  =							torch.hub.load('''pytorch/fairseq'''						,    lowerCamelCase_			).eval()
			else:
						__lowercase			  =							load_xsum_checkpoint(lowerCamelCase_			)

			bart.model.upgrade_state_dict(bart.model.state_dict()			)
			if hf_checkpoint_name is None:
						__lowercase			  =							checkpoint_path.replace('''.'''						,    '''-'''			)
			__lowercase			  =							BartConfig.from_pretrained(lowerCamelCase_			)
			__lowercase			  =							bart.encode(lowerCamelCase_			).unsqueeze(0			)
			__lowercase			  =							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":
						__lowercase			  =							bart.state_dict()
						remove_ignore_keys_(lowerCamelCase_			)
						__lowercase			  =							state_dict['''model.decoder.embed_tokens.weight''']
						for src, dest in mnli_rename_keys:
									rename_key(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
						__lowercase			  =							BartForSequenceClassification(lowerCamelCase_			).eval()
						model.load_state_dict(lowerCamelCase_			)
						__lowercase			  =							bart.predict('''mnli'''						,    lowerCamelCase_						,    return_logits=lowerCamelCase_			)
						__lowercase			  =							model(lowerCamelCase_			)[0]  # logits
			else:  # no classification heads to worry about
						__lowercase			  =							bart.model.state_dict()
						remove_ignore_keys_(lowerCamelCase_			)
						__lowercase			  =							state_dict['''decoder.embed_tokens.weight''']
						__lowercase			  =							bart.extract_features(lowerCamelCase_			)
						if hf_checkpoint_name == "facebook/bart-large":
									__lowercase			  =							BartModel(lowerCamelCase_			).eval()
									model.load_state_dict(lowerCamelCase_			)
									__lowercase			  =							model(lowerCamelCase_			).model[0]
						else:
									__lowercase			  =							BartForConditionalGeneration(lowerCamelCase_			).eval()  # an existing summarization ckpt
									model.model.load_state_dict(lowerCamelCase_			)
									if hasattr(lowerCamelCase_						,    '''lm_head'''			):
												__lowercase			  =							make_linear_from_emb(model.model.shared			)
									__lowercase			  =							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)





