






"""simple docstring"""



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class  _UpperCAmelCase	(			_A       ,						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_		:    int							   =       BartTokenizer
     SCREAMING_SNAKE_CASE_		:    Tuple							   =       BartTokenizerFast
     SCREAMING_SNAKE_CASE_		:    Union[str, Any]							   =       True
     SCREAMING_SNAKE_CASE_		:    Dict							   =       filter_roberta_detectors




     def   A  (    self					:						Optional[int]       )  ->  List[str]:
       super().setUp()
       lowercase_			:			int								=       [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       lowercase_			:			Dict								=       dict(zip(A   ,						range(len(A       )       )       )       )
       lowercase_			:			Dict								=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       lowercase_			:			Dict								=       {'''unk_token''': '''<unk>'''}

       lowercase_			:			Any								=       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''vocab_file''']       )
       lowercase_			:			Tuple								=       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''merges_file''']       )
       with open(self.vocab_file   ,						'''w'''   ,						encoding='''utf-8'''       ) as fp:
         fp.write(json.dumps(A       ) + '''\n'''       )
       with open(self.merges_file   ,						'''w'''   ,						encoding='''utf-8'''       ) as fp:
         fp.write('''\n'''.join(A       )       )




     def   A  (    self					:						List[str]   ,						**A					:						List[str]       )  ->  List[str]:
       kwargs.update(self.special_tokens_map       )
       return self.tokenizer_class.from_pretrained(self.tmpdirname   ,						**A       )




     def   A  (    self					:						List[str]   ,						**A					:						str       )  ->  int:
       kwargs.update(self.special_tokens_map       )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,						**A       )




     def   A  (    self					:						List[Any]   ,						A					:						List[str]       )  ->  Dict:
       return "lower newer", "lower newer"




     @cached_property
     def   A  (    self					:						Tuple       )  ->  List[str]:
       return BartTokenizer.from_pretrained('''facebook/bart-large'''       )




     @cached_property
     def   A  (    self					:						str       )  ->  Optional[Any]:
       return BartTokenizerFast.from_pretrained('''facebook/bart-large'''       )




     @require_torch
     def   A  (    self					:						Any       )  ->  List[str]:
       lowercase_			:			Tuple								=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase_			:			Tuple								=       [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         lowercase_			:			Any								=       tokenizer(A   ,						max_length=len(A       )   ,						padding=A   ,						return_tensors='''pt'''       )
         self.assertIsInstance(A   ,						A       )

         self.assertEqual((2, 9)   ,						batch.input_ids.shape       )
         self.assertEqual((2, 9)   ,						batch.attention_mask.shape       )
         lowercase_			:			int								=       batch.input_ids.tolist()[0]
         self.assertListEqual(A   ,						A       )
         # Test that special tokens are reset




     @require_torch
     def   A  (    self					:						Tuple       )  ->  Optional[Any]:
       lowercase_			:			List[str]								=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         lowercase_			:			List[Any]								=       tokenizer(A   ,						padding=A   ,						return_tensors='''pt'''       )
         # check if input_ids are returned and no labels
         self.assertIn('''input_ids'''   ,						A       )
         self.assertIn('''attention_mask'''   ,						A       )
         self.assertNotIn('''labels'''   ,						A       )
         self.assertNotIn('''decoder_attention_mask'''   ,						A       )




     @require_torch
     def   A  (    self					:						Optional[int]       )  ->  Optional[Any]:
       lowercase_			:			List[str]								=       [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         lowercase_			:			Optional[int]								=       tokenizer(text_target=A   ,						max_length=32   ,						padding='''max_length'''   ,						return_tensors='''pt'''       )
         self.assertEqual(32   ,						targets['''input_ids'''].shape[1]       )




     @require_torch
     def   A  (    self					:						List[Any]       )  ->  Any:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         lowercase_			:			Any								=       tokenizer(
             ['''I am a small frog''' * 10_24, '''I am a small frog''']   ,						padding=A   ,						truncation=A   ,						return_tensors='''pt'''       )
         self.assertIsInstance(A   ,						A       )
         self.assertEqual(batch.input_ids.shape   ,						(2, 10_24)       )




     @require_torch
     def   A  (    self					:						int       )  ->  str:
       lowercase_			:			Optional[int]								=       ['''A long paragraph for summarization.''']
       lowercase_			:			Optional[int]								=       [
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         lowercase_			:			str								=       tokenizer(A   ,						return_tensors='''pt'''       )
         lowercase_			:			List[Any]								=       tokenizer(text_target=A   ,						return_tensors='''pt'''       )
         lowercase_			:			str								=       inputs['''input_ids''']
         lowercase_			:			Optional[Any]								=       targets['''input_ids''']
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )




     def   A  (    self					:						Optional[Any]       )  ->  Tuple:
       pass






     def   A  (    self					:						int       )  ->  int:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
           lowercase_			:			Dict								=       self.rust_tokenizer_class.from_pretrained(A   ,						**A       )
           lowercase_			:			int								=       self.tokenizer_class.from_pretrained(A   ,						**A       )
           lowercase_			:			List[Any]								=       '''A, <mask> AllenNLP sentence.'''
           lowercase_			:			Tuple								=       tokenizer_r.encode_plus(A   ,						add_special_tokens=A   ,						return_token_type_ids=A       )
           lowercase_			:			Tuple								=       tokenizer_p.encode_plus(A   ,						add_special_tokens=A   ,						return_token_type_ids=A       )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r['''token_type_ids''']       )   ,						sum(tokens_p['''token_type_ids''']       )       )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r['''attention_mask''']       ) / len(tokens_r['''attention_mask''']       )   ,						sum(tokens_p['''attention_mask''']       ) / len(tokens_p['''attention_mask''']       )   ,						)

           lowercase_			:			Union[str, Any]								=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
           lowercase_			:			Dict								=       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']       )

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p['''input_ids''']   ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )
           self.assertSequenceEqual(tokens_r['''input_ids''']   ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )

           self.assertSequenceEqual(
               A   ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )
           self.assertSequenceEqual(
               A   ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )











"""simple docstring"""



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A		:	Union[str, Any]      =		{512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}



def 						lowercase   (    __snake_case    :			Optional[int]     ):
		lowercase_			:			Any								=       F'''pretrained_models/{model_name}'''
		if not os.path.isfile(__snake_case     ):
				os.makedirs('''pretrained_models'''		, exist_ok=__snake_case     )
				lowercase_			:			str								=       F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
				download_url(__snake_case		, '''pretrained_models'''     )
		lowercase_			:			List[str]								=       torch.load(__snake_case		, map_location=lambda __snake_case		, __snake_case     : storage     )
		return model



def 						lowercase   (    __snake_case    :			Optional[int]     ):
		lowercase_			:			Dict								=       download_model(pretrained_models[args.image_size]     )

		lowercase_			:			List[str]								=       state_dict['''x_embedder.proj.weight''']
		lowercase_			:			Tuple								=       state_dict['''x_embedder.proj.bias''']
		state_dict.pop('''x_embedder.proj.weight'''     )
		state_dict.pop('''x_embedder.proj.bias'''     )

		for depth in range(2_8     ):
				lowercase_			:			Optional[int]								=       state_dict[
				    '''t_embedder.mlp.0.weight'''
				]
				lowercase_			:			List[str]								=       state_dict[
				    '''t_embedder.mlp.0.bias'''
				]
				lowercase_			:			Tuple								=       state_dict[
				    '''t_embedder.mlp.2.weight'''
				]
				lowercase_			:			Union[str, Any]								=       state_dict[
				    '''t_embedder.mlp.2.bias'''
				]
				lowercase_			:			Dict								=       state_dict[
				    '''y_embedder.embedding_table.weight'''
				]

				lowercase_			:			List[str]								=       state_dict[
				    F'''blocks.{depth}.adaLN_modulation.1.weight'''
				]
				lowercase_			:			Optional[Any]								=       state_dict[
				    F'''blocks.{depth}.adaLN_modulation.1.bias'''
				]

				lowercase_	, lowercase_	, lowercase_			:			List[Any]								=       torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']		, 3		, dim=0     )
				lowercase_	, lowercase_	, lowercase_			:			Optional[int]								=       torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']		, 3		, dim=0     )

				lowercase_			:			Union[str, Any]								=       q
				lowercase_			:			Dict								=       q_bias
				lowercase_			:			Dict								=       k
				lowercase_			:			Tuple								=       k_bias
				lowercase_			:			str								=       v
				lowercase_			:			Union[str, Any]								=       v_bias

				lowercase_			:			Optional[Any]								=       state_dict[
				    F'''blocks.{depth}.attn.proj.weight'''
				]
				lowercase_			:			List[Any]								=       state_dict[F'''blocks.{depth}.attn.proj.bias''']

				lowercase_			:			List[Any]								=       state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
				lowercase_			:			Optional[Any]								=       state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
				lowercase_			:			Tuple								=       state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
				lowercase_			:			List[str]								=       state_dict[F'''blocks.{depth}.mlp.fc2.bias''']

				state_dict.pop(F'''blocks.{depth}.attn.qkv.weight'''     )
				state_dict.pop(F'''blocks.{depth}.attn.qkv.bias'''     )
				state_dict.pop(F'''blocks.{depth}.attn.proj.weight'''     )
				state_dict.pop(F'''blocks.{depth}.attn.proj.bias'''     )
				state_dict.pop(F'''blocks.{depth}.mlp.fc1.weight'''     )
				state_dict.pop(F'''blocks.{depth}.mlp.fc1.bias'''     )
				state_dict.pop(F'''blocks.{depth}.mlp.fc2.weight'''     )
				state_dict.pop(F'''blocks.{depth}.mlp.fc2.bias'''     )
				state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.weight'''     )
				state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.bias'''     )

		state_dict.pop('''t_embedder.mlp.0.weight'''     )
		state_dict.pop('''t_embedder.mlp.0.bias'''     )
		state_dict.pop('''t_embedder.mlp.2.weight'''     )
		state_dict.pop('''t_embedder.mlp.2.bias'''     )
		state_dict.pop('''y_embedder.embedding_table.weight'''     )

		lowercase_			:			Union[str, Any]								=       state_dict['''final_layer.adaLN_modulation.1.weight''']
		lowercase_			:			Optional[Any]								=       state_dict['''final_layer.adaLN_modulation.1.bias''']
		lowercase_			:			Dict								=       state_dict['''final_layer.linear.weight''']
		lowercase_			:			Union[str, Any]								=       state_dict['''final_layer.linear.bias''']

		state_dict.pop('''final_layer.linear.weight'''     )
		state_dict.pop('''final_layer.linear.bias'''     )
		state_dict.pop('''final_layer.adaLN_modulation.1.weight'''     )
		state_dict.pop('''final_layer.adaLN_modulation.1.bias'''     )

		# DiT XL/2
		lowercase_			:			List[str]								=       TransformeraDModel(
		    sample_size=args.image_size // 8		, num_layers=2_8		, attention_head_dim=7_2		, in_channels=4		, out_channels=8		, patch_size=2		, attention_bias=__snake_case		, num_attention_heads=1_6		, activation_fn='''gelu-approximate'''		, num_embeds_ada_norm=1_0_0_0		, norm_type='''ada_norm_zero'''		, norm_elementwise_affine=__snake_case		, )
		transformer.load_state_dict(__snake_case		, strict=__snake_case     )

		lowercase_			:			Any								=       DDIMScheduler(
		    num_train_timesteps=1_0_0_0		, beta_schedule='''linear'''		, prediction_type='''epsilon'''		, clip_sample=__snake_case		, )

		lowercase_			:			Dict								=       AutoencoderKL.from_pretrained(args.vae_model     )

		lowercase_			:			str								=       DiTPipeline(transformer=__snake_case		, vae=__snake_case		, scheduler=__snake_case     )

		if args.save:
				pipeline.save_pretrained(args.checkpoint_path     )


if __name__ == "__main__":
						__A		:	int      =		argparse.ArgumentParser()

						parser.add_argument(
						    '''--image_size''',
						    default=256,
						    type=int,
						    required=False,
						    help='''Image size of pretrained model, either 256 or 512.''',
						)
						parser.add_argument(
						    '''--vae_model''',
						    default='''stabilityai/sd-vae-ft-ema''',
						    type=str,
						    required=False,
						    help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
						)
						parser.add_argument(
						    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
						)
						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
						)

						__A		:	Union[str, Any]      =		parser.parse_args()
						main(args)



