






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowerCAmelCase_						:     int							  =   False





class 	UpperCamelCase_			(							unittest.TestCase	):



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





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



  @property
  def 		UpperCamelCase_					(   self					)      ->      str:



         """simple docstring"""





         return 12



  @property
  def 		UpperCamelCase_					(   self					)      ->      List[Any]:



         """simple docstring"""





         return 12



  @property
  def 		UpperCamelCase_					(   self					)      ->      Any:



         """simple docstring"""





         return 32



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



         """simple docstring"""





         torch.manual_seed(0					)
         UpperCAmelCase						     =      VQModel(
             block_out_channels=[32, 64]			,		in_channels=3			,		out_channels=3			,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,		latent_channels=3			,		num_vq_embeddings=self.num_embed			,		vq_embed_dim=3			,		)
         return model



  @property
  def 		UpperCamelCase_					(   self					)      ->      Tuple:



         """simple docstring"""





         UpperCAmelCase						     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)
         return tokenizer



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



         """simple docstring"""





         torch.manual_seed(0					)
         UpperCAmelCase						     =      CLIPTextConfig(
             bos_token_id=0			,		eos_token_id=2			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		layer_norm_eps=1e-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=10_00			,		)
         return CLIPTextModel(lowerCAmelCase__					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      Dict:



         """simple docstring"""





         torch.manual_seed(0					)

         UpperCAmelCase						     =      12
         UpperCAmelCase						     =      12

         UpperCAmelCase						     =      {
             'attention_bias': True,
             'cross_attention_dim': 32,
             'attention_head_dim': height * width,
             'num_attention_heads': 1,
             'num_vector_embeds': self.num_embed,
             'num_embeds_ada_norm': self.num_embeds_ada_norm,
             'norm_num_groups': 32,
             'sample_size': width,
             'activation_fn': 'geglu-approximate',
         }

         UpperCAmelCase						     =      TransformeraDModel(**lowerCAmelCase__					)
         return model



  def 		UpperCamelCase_					(   self					)      ->      Tuple:



         """simple docstring"""





         UpperCAmelCase						     =      'cpu'

         UpperCAmelCase						     =      self.dummy_vqvae
         UpperCAmelCase						     =      self.dummy_text_encoder
         UpperCAmelCase						     =      self.dummy_tokenizer
         UpperCAmelCase						     =      self.dummy_transformer
         UpperCAmelCase						     =      VQDiffusionScheduler(self.num_embed					)
         UpperCAmelCase						     =      LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__					)

         UpperCAmelCase						     =      VQDiffusionPipeline(
             vqvae=lowerCAmelCase__			,		text_encoder=lowerCAmelCase__			,		tokenizer=lowerCAmelCase__			,		transformer=lowerCAmelCase__			,		scheduler=lowerCAmelCase__			,		learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,		)
         UpperCAmelCase						     =      pipe.to(lowerCAmelCase__					)
         pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

         UpperCAmelCase						     =      'teddy bear playing in the pool'

         UpperCAmelCase						     =      torch.Generator(device=lowerCAmelCase__					).manual_seed(0					)
         UpperCAmelCase						     =      pipe([prompt]			,		generator=lowerCAmelCase__			,		num_inference_steps=2			,		output_type="""np"""					)
         UpperCAmelCase						     =      output.images

         UpperCAmelCase						     =      torch.Generator(device=lowerCAmelCase__					).manual_seed(0					)
         UpperCAmelCase						     =      pipe(
             [prompt]			,		generator=lowerCAmelCase__			,		output_type="""np"""			,		return_dict=lowerCAmelCase__			,		num_inference_steps=2					)[0]

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

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase						     =      np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]					)

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



  def 		UpperCamelCase_					(   self					)      ->      Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase						     =      'cpu'

         UpperCAmelCase						     =      self.dummy_vqvae
         UpperCAmelCase						     =      self.dummy_text_encoder
         UpperCAmelCase						     =      self.dummy_tokenizer
         UpperCAmelCase						     =      self.dummy_transformer
         UpperCAmelCase						     =      VQDiffusionScheduler(self.num_embed					)
         UpperCAmelCase						     =      LearnedClassifierFreeSamplingEmbeddings(
             learnable=lowerCAmelCase__			,		hidden_size=self.text_embedder_hidden_size			,		length=tokenizer.model_max_length					)

         UpperCAmelCase						     =      VQDiffusionPipeline(
             vqvae=lowerCAmelCase__			,		text_encoder=lowerCAmelCase__			,		tokenizer=lowerCAmelCase__			,		transformer=lowerCAmelCase__			,		scheduler=lowerCAmelCase__			,		learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,		)
         UpperCAmelCase						     =      pipe.to(lowerCAmelCase__					)
         pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

         UpperCAmelCase						     =      'teddy bear playing in the pool'

         UpperCAmelCase						     =      torch.Generator(device=lowerCAmelCase__					).manual_seed(0					)
         UpperCAmelCase						     =      pipe([prompt]			,		generator=lowerCAmelCase__			,		num_inference_steps=2			,		output_type="""np"""					)
         UpperCAmelCase						     =      output.images

         UpperCAmelCase						     =      torch.Generator(device=lowerCAmelCase__					).manual_seed(0					)
         UpperCAmelCase						     =      pipe(
             [prompt]			,		generator=lowerCAmelCase__			,		output_type="""np"""			,		return_dict=lowerCAmelCase__			,		num_inference_steps=2					)[0]

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

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase						     =      np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]					)

         assert np.abs(image_slice.flatten() - expected_slice					).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2





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



  def 		UpperCamelCase_					(   self					)      ->      Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_					(   self					)      ->      Optional[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""					)

         UpperCAmelCase						     =      VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""					)
         UpperCAmelCase						     =      pipeline.to(lowerCAmelCase__					)
         pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         UpperCAmelCase						     =      torch.Generator(device=lowerCAmelCase__					).manual_seed(0					)
         UpperCAmelCase						     =      pipeline(
             """teddy bear playing in the pool"""			,		num_images_per_prompt=1			,		generator=lowerCAmelCase__			,		output_type="""np"""			,		)

         UpperCAmelCase						     =      output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image					).max() < 2.0




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    '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(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      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',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								=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.')
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)





