






'''simple docstring'''
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   __SCREAMING_SNAKE_CASE   (      __UpperCAmelCase						,					unittest.TestCase					):
 snake_case_					=				KandinskyVaaPriorPipeline
 snake_case_					=				['prompt']
 snake_case_					=				['prompt', 'negative_prompt']
 snake_case_					=				[
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 snake_case_					=				False




 @property
 def   __magic_name__						(   self						:				Optional[Any]   )						->    List[str]:
       return 32




 @property
 def   __magic_name__						(   self						:				Dict   )						->    Dict:
       return 32




 @property
 def   __magic_name__						(   self						:				int   )						->    str:
       return self.time_input_dim




 @property
 def   __magic_name__						(   self						:				Optional[Any]   )						->    Optional[Any]:
       return self.time_input_dim * 4




 @property
 def   __magic_name__						(   self						:				List[str]   )						->    Any:
       return 1_00




 @property
 def   __magic_name__						(   self						:				Any   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer




 @property
 def   __magic_name__						(   self						:				Optional[Any]   )						->    str:
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					Dict						=CLIPTextConfig(
           bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=self.text_embedder_hidden_size       ,   projection_dim=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 CLIPTextModelWithProjection(lowerCAmelCase_   )




 @property
 def   __magic_name__						(   self						:				Optional[int]   )						->    Optional[int]:
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE__  :					List[str]						={
           'num_attention_heads': 2,
           'attention_head_dim': 12,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=PriorTransformer(**lowerCAmelCase_   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       SCREAMING_SNAKE_CASE__  :					Tuple						=nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model




 @property
 def   __magic_name__						(   self						:				List[Any]   )						->    Dict:
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size       ,   image_size=2_24       ,   projection_dim=self.text_embedder_hidden_size       ,   intermediate_size=37       ,   num_attention_heads=4       ,   num_channels=3       ,   num_hidden_layers=5       ,   patch_size=14       ,   )

       SCREAMING_SNAKE_CASE__  :					int						=CLIPVisionModelWithProjection(lowerCAmelCase_   )
       return model




 @property
 def   __magic_name__						(   self						:				Tuple   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					List[str]						=CLIPImageProcessor(
           crop_size=2_24       ,   do_center_crop=lowerCAmelCase_       ,   do_normalize=lowerCAmelCase_       ,   do_resize=lowerCAmelCase_       ,   image_mean=[0.48145466, 0.4578275, 0.40821073]       ,   image_std=[0.26862954, 0.26130258, 0.27577711]       ,   resample=3       ,   size=2_24       ,   )

       return image_processor




 def   __magic_name__						(   self						:				List[str]   )						->    str:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=self.dummy_prior
       SCREAMING_SNAKE_CASE__  :					Tuple						=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__  :					int						=self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__  :					Dict						=self.dummy_image_processor

       SCREAMING_SNAKE_CASE__  :					List[str]						=UnCLIPScheduler(
           variance_type='''fixed_small_log'''       ,   prediction_type='''sample'''       ,   num_train_timesteps=10_00       ,   clip_sample=lowerCAmelCase_       ,   clip_sample_range=10.0       ,   )

       SCREAMING_SNAKE_CASE__  :					Tuple						={
           'prior': prior,
           'image_encoder': image_encoder,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'scheduler': scheduler,
           'image_processor': image_processor,
       }

       return components




 def   __magic_name__						(   self						:				List[str]       ,   __lowercase						:				Union[str, Any]       ,   __lowercase						:				Optional[Any]=0   )						->    Dict:
       if str(lowerCAmelCase_   ).startswith('''mps'''   ):
             SCREAMING_SNAKE_CASE__  :					List[Any]						=torch.manual_seed(lowerCAmelCase_   )
       else:
             SCREAMING_SNAKE_CASE__  :					Any						=torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						={
           'prompt': 'horse',
           'generator': generator,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs




 def   __magic_name__						(   self						:				List[Any]   )						->    str:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						='cpu'

       SCREAMING_SNAKE_CASE__  :					int						=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=self.pipeline_class(**lowerCAmelCase_   )
       SCREAMING_SNAKE_CASE__  :					Dict						=pipe.to(lowerCAmelCase_   )

       pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=pipe(**self.get_dummy_inputs(lowerCAmelCase_   )   )
       SCREAMING_SNAKE_CASE__  :					Tuple						=output.image_embeds

       SCREAMING_SNAKE_CASE__  :					int						=pipe(
           **self.get_dummy_inputs(lowerCAmelCase_   )       ,   return_dict=lowerCAmelCase_       ,   )[0]

       SCREAMING_SNAKE_CASE__  :					List[str]						=image[0, -10:]
       SCREAMING_SNAKE_CASE__  :					Dict						=image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       SCREAMING_SNAKE_CASE__  :					str						=np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]   )

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




 @skip_mps
 def   __magic_name__						(   self						:				int   )						->    List[str]:
       SCREAMING_SNAKE_CASE__  :					int						=torch_device == 'cpu'
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=True
       SCREAMING_SNAKE_CASE__  :					Tuple						=False

       self._test_inference_batch_single_identical(
           test_max_difference=lowerCAmelCase_       ,   relax_max_difference=lowerCAmelCase_       ,   test_mean_pixel_difference=lowerCAmelCase_       ,   )




 @skip_mps
 def   __magic_name__						(   self						:				Optional[int]   )						->    int:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=torch_device == 'cpu'
       SCREAMING_SNAKE_CASE__  :					List[str]						=False

       self._test_attention_slicing_forward_pass(
           test_max_difference=lowerCAmelCase_       ,   test_mean_pixel_difference=lowerCAmelCase_       ,   )







"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

