import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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      lowerCamelCase_					(       _UpperCAmelCase						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Any      =				KandinskyImgaImgPipeline
    a__     :						Tuple      =				['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    a__     :						str      =				[
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    a__     :						Union[str, Any]      =				[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    a__     :						Dict      =				False







    @property
    def       UpperCamelCase__       (     self)	->     List[Any]:
        return 32







    @property
    def       UpperCamelCase__       (     self)	->     Tuple:
        return 32







    @property
    def       UpperCamelCase__       (     self)	->     Any:
        return self.time_input_dim







    @property
    def       UpperCamelCase__       (     self)	->     List[str]:
        return self.time_input_dim * 4







    @property
    def       UpperCamelCase__       (     self)	->     Tuple:
        return 100







    @property
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[str]        =		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
        return tokenizer







    @property
    def       UpperCamelCase__       (     self)	->     int:
        torch.manual_seed(0)
        __UpperCamelCase					:Optional[Any]        =		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			,			)

        __UpperCamelCase					:int        =		MultilingualCLIP(SCREAMING_SNAKE_CASE_)
        __UpperCamelCase					:Union[str, Any]        =		text_encoder.eval()

        return text_encoder







    @property
    def       UpperCamelCase__       (     self)	->     Dict:
        torch.manual_seed(0)

        __UpperCamelCase					:List[Any]        =		{
            """in_channels""": 4,
            # 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,
        }

        __UpperCamelCase					:Optional[int]        =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
        return model







    @property
    def       UpperCamelCase__       (     self)	->     List[str]:
        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)	->     Optional[int]:
        torch.manual_seed(0)
        __UpperCamelCase					:Any        =		VQModel(**self.dummy_movq_kwargs)
        return model







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Optional[int]        =		self.dummy_text_encoder
        __UpperCamelCase					:Optional[int]        =		self.dummy_tokenizer
        __UpperCamelCase					:Union[str, Any]        =		self.dummy_unet
        __UpperCamelCase					:Optional[Any]        =		self.dummy_movq

        __UpperCamelCase					:List[str]        =		{
            """num_train_timesteps""": 1_000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.0_00_85,
            """beta_end""": 0.0_12,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        __UpperCamelCase					:Optional[Any]        =		DDIMScheduler(**SCREAMING_SNAKE_CASE_)

        __UpperCamelCase					:Tuple        =		{
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=0)	->     Optional[int]:
        __UpperCamelCase					:Any        =		floats_tensor((1, self.cross_attention_dim)			,			rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
        __UpperCamelCase					:Optional[Any]        =		floats_tensor((1, self.cross_attention_dim)			,			rng=random.Random(seed + 1)).to(SCREAMING_SNAKE_CASE_)
        # create init_image
        __UpperCamelCase					:Dict        =		floats_tensor((1, 3, 64, 64)			,			rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
        __UpperCamelCase					:int        =		image.cpu().permute(0			,			2			,			3			,			1)[0]
        __UpperCamelCase					:Optional[int]        =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_)).convert('''RGB''').resize((256, 256))

        if str(SCREAMING_SNAKE_CASE_).startswith('''mps'''):
            __UpperCamelCase					:int        =		torch.manual_seed(SCREAMING_SNAKE_CASE_)
        else:
            __UpperCamelCase					:int        =		torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
        __UpperCamelCase					:Optional[int]        =		{
            """prompt""": """horse""",
            """image""": init_image,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 10,
            """guidance_scale""": 7.0,
            """strength""": 0.2,
            """output_type""": """np""",
        }
        return inputs







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:Tuple        =		"""cpu"""

        __UpperCamelCase					:Dict        =		self.get_dummy_components()

        __UpperCamelCase					:Optional[int]        =		self.pipeline_class(**SCREAMING_SNAKE_CASE_)
        __UpperCamelCase					:List[str]        =		pipe.to(SCREAMING_SNAKE_CASE_)

        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

        __UpperCamelCase					:Optional[Any]        =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_))
        __UpperCamelCase					:Optional[Any]        =		output.images

        __UpperCamelCase					:int        =		pipe(
            **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)			,			return_dict=SCREAMING_SNAKE_CASE_			,			)[0]

        __UpperCamelCase					:Tuple        =		image[0, -3:, -3:, -1]
        __UpperCamelCase					:str        =		image_from_tuple[0, -3:, -3:, -1]

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

        __UpperCamelCase					:Dict        =		np.array(
            [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33])
        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()}"""


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






    '''simple docstring'''






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







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[str]        =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinsky/kandinsky_img2img_frog.npy''')

        __UpperCamelCase					:Dict        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
        __UpperCamelCase					:List[Any]        =		"""A red cartoon frog, 4k"""

        __UpperCamelCase					:List[Any]        =		KandinskyPriorPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-1-prior'''			,			torch_dtype=torch.floataa)
        pipe_prior.to(SCREAMING_SNAKE_CASE_)

        __UpperCamelCase					:List[Any]        =		KandinskyImgaImgPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-1'''			,			torch_dtype=torch.floataa)
        __UpperCamelCase					:List[str]        =		pipeline.to(SCREAMING_SNAKE_CASE_)

        pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

        __UpperCamelCase					:int        =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase					:Optional[int]        =		pipe_prior(
            SCREAMING_SNAKE_CASE_			,			generator=SCREAMING_SNAKE_CASE_			,			num_inference_steps=5			,			negative_prompt=''''''			,			).to_tuple()

        __UpperCamelCase					:Optional[int]        =		pipeline(
            SCREAMING_SNAKE_CASE_			,			image=SCREAMING_SNAKE_CASE_			,			image_embeds=SCREAMING_SNAKE_CASE_			,			negative_image_embeds=SCREAMING_SNAKE_CASE_			,			generator=SCREAMING_SNAKE_CASE_			,			num_inference_steps=100			,			height=768			,			width=768			,			strength=0.2			,			output_type='''np'''			,			)

        __UpperCamelCase					:str        =		output.images[0]

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

        assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_			,			SCREAMING_SNAKE_CASE_)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      encoder_layers=args.encoder_layers  ,      decoder_layers=args.decoder_layers  ,      encoder_attention_heads=args.encoder_attention_heads  ,      decoder_attention_heads=args.decoder_attention_heads  ,      encoder_ffn_dim=args.encoder_ffn_embed_dim  ,      decoder_ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.encoder_embed_dim  ,      encoder_layerdrop=args.encoder_layerdrop  ,      decoder_layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''relu'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





