
"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   MvpTokenizer
				__UpperCamelCase      :						Any					       =   MvpTokenizerFast
				__UpperCamelCase      :						Optional[int]					       =   True
				__UpperCamelCase      :						int					       =   filter_roberta_detectors



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											super().setUp()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   dict(zip(SCREAMING_SNAKE_CASE__   ,       range(len(SCREAMING_SNAKE_CASE__    )    )    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__						:       str           =   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   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(SCREAMING_SNAKE_CASE__    ) + """\n"""    )
											with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""    ) as fp:
																		fp.write("""\n""".join(SCREAMING_SNAKE_CASE__    )    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											kwargs.update(self.special_tokens_map    )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return "lower newer", "lower newer"



				@cached_property
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )



				@cached_property
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )



				@require_torch
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   [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]:
																		SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       max_length=len(SCREAMING_SNAKE_CASE__    )   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

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



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		# check if input_ids are returned and no labels
																		self.assertIn("""input_ids"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertIn("""attention_mask"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertNotIn("""labels"""   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertNotIn("""decoder_attention_mask"""   ,       SCREAMING_SNAKE_CASE__    )



				@require_torch
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   [
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer(text_target=SCREAMING_SNAKE_CASE__   ,       max_length=32   ,       padding="""max_length"""   ,       return_tensors="""pt"""    )
																		self.assertEqual(32   ,       targets["""input_ids"""].shape[1]    )



				@require_torch
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer(
																		    ["""I am a small frog""" * 10_24, """I am a small frog"""]   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		self.assertEqual(batch.input_ids.shape   ,       (2, 10_24)    )



				@require_torch
				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ["""A long paragraph for summarization."""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       text_target=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   inputs["""input_ids"""]
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   inputs["""labels"""]
																		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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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

																									# 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"""]    )   ,       )

																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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(
																									    SCREAMING_SNAKE_CASE__   ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																									self.assertSequenceEqual(
																									    SCREAMING_SNAKE_CASE__   ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )






"""simple docstring"""




import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def lowercase_  (   _snake_case						):
							# encoder.embeddings are double copied in original FLAVA
							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()						)

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Any           =   {}

							for key, value in state_dict.items():
														if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
																					continue

														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   key.replace("""heads.cmd.mim_head.cls.predictions"""   ,"""mmm_image_head"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""heads.cmd.mlm_head.cls.predictions"""   ,"""mmm_text_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""heads.cmd.itm_head.cls"""   ,"""itm_head"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.cmd.itm_head.pooler"""   ,"""itm_head.pooler"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   key.replace("""heads.cmd.clip_head.logit_scale"""   ,"""flava.logit_scale"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.fairseq_mlm.cls.predictions"""   ,"""mlm_head"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""heads.imagenet.mim_head.cls.predictions"""   ,"""mim_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   key.replace("""mm_text_projection"""   ,"""flava.text_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""mm_image_projection"""   ,"""flava.image_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""image_encoder.module"""   ,"""flava.image_model"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""text_encoder.module"""   ,"""flava.text_model"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   key.replace("""mm_encoder.module.encoder.cls_token"""   ,"""flava.multimodal_model.cls_token"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""mm_encoder.module"""   ,"""flava.multimodal_model"""						)
														SCREAMING_SNAKE_CASE__						:       Any           =   key.replace("""text_projection"""   ,"""flava.text_projection"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""image_projection"""   ,"""flava.image_projection"""						)

														SCREAMING_SNAKE_CASE__						:       Tuple           =   value.float()

							for key, value in codebook_state_dict.items():
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value

							return upgrade

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=None						):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   FlavaConfig.from_pretrained(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   FlavaConfig()

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FlavaForPreTraining(_snake_case						).eval()

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   convert_dalle_checkpoint(_snake_case   ,_snake_case   ,save_checkpoint=_snake_case						)

							if os.path.exists(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.load(_snake_case   ,map_location="""cpu"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   upgrade_state_dict(_snake_case   ,_snake_case						)
							hf_model.load_state_dict(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Any           =   hf_model.state_dict()
							SCREAMING_SNAKE_CASE__						:       Any           =   count_parameters(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       str           =   count_parameters(_snake_case						) + count_parameters(_snake_case						)

							assert torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3						)

							hf_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    List[Any]    							= argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					UpperCAmelCase__      :    Optional[int]    							= parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




