




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__         =      get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")
@require_sentencepiece
@require_tokenizers
class       a__   ( snake_case__			,    unittest.TestCase				):
						_a							:   List[Any] =   PegasusTokenizer
						_a							:   Optional[int] =   PegasusTokenizerFast
						_a							:   str =   True
						_a							:   Union[str, Any] =   True






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase           =	PegasusTokenizer(_A      )
											tokenizer.save_pretrained(self.tmpdirname      )






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return PegasusTokenizer.from_pretrained("google/pegasus-large"      )






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return ("This is a test", "This is a test")






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"</s>"
											__lowerCAmelCase           =	1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A      )		,      _A      )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A      )		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	list(self.get_tokenizer().get_vocab().keys()      )

											self.assertEqual(vocab_keys[0]		,      "<pad>"      )
											self.assertEqual(vocab_keys[1]		,      "</s>"      )
											self.assertEqual(vocab_keys[-1]		,      "v"      )
											self.assertEqual(len(_A      )		,      1_1_0_3      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.assertEqual(self.get_tokenizer().vocab_size		,      1_1_0_3      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
											__lowerCAmelCase           =	self.tokenizer_class.from_pretrained(self.tmpdirname      )
											__lowerCAmelCase           =	(
											    "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
											    " </s> <pad> <pad> <pad>"
											)
											__lowerCAmelCase           =	rust_tokenizer([raw_input_str]		,      return_tensors=_A		,      add_special_tokens=_A      ).input_ids[0]
											__lowerCAmelCase           =	py_tokenizer([raw_input_str]		,      return_tensors=_A		,      add_special_tokens=_A      ).input_ids[0]
											self.assertListEqual(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._large_tokenizer
											# <mask_1> masks whole sentence while <mask_2> masks single word
											__lowerCAmelCase           =	"<mask_1> To ensure a <mask_2> flow of bank resolutions."
											__lowerCAmelCase           =	[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
											__lowerCAmelCase           =	tokenizer([raw_input_str]		,      return_tensors=_A      ).input_ids[0]
											self.assertListEqual(_A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._large_tokenizer
											# The tracebacks for the following asserts are **better** without messages or self.assertEqual
											assert tokenizer.vocab_size == 9_6_1_0_3
											assert tokenizer.pad_token_id == 0
											assert tokenizer.eos_token_id == 1
											assert tokenizer.offset == 1_0_3
											assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
											assert tokenizer.unk_token == "<unk>"
											assert tokenizer.model_max_length == 1_0_2_4
											__lowerCAmelCase           =	"To ensure a smooth flow of bank resolutions."
											__lowerCAmelCase           =	[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
											__lowerCAmelCase           =	tokenizer([raw_input_str]		,      return_tensors=_A      ).input_ids[0]
											self.assertListEqual(_A		,      _A      )
											assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	["This is going to be way too long." * 1_5_0, "short example"]
											__lowerCAmelCase           =	["not super long but more than 5 tokens", "tiny"]
											__lowerCAmelCase           =	self._large_tokenizer(_A		,      padding=_A		,      truncation=_A		,      return_tensors="pt"      )
											__lowerCAmelCase           =	self._large_tokenizer(
											    text_target=_A		,      max_length=5		,      padding=_A		,      truncation=_A		,      return_tensors="pt"      )

											assert batch.input_ids.shape == (2, 1_0_2_4)
											assert batch.attention_mask.shape == (2, 1_0_2_4)
											assert targets["input_ids"].shape == (2, 5)
											assert len(_A      ) == 2  # input_ids, attention_mask.






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=_A		,      model_name="google/bigbird-pegasus-large-arxiv"		,      revision="ba85d0851d708441f91440d509690f1ab6353415"		,      )






@require_sentencepiece
@require_tokenizers
class       a__   ( snake_case__			,    unittest.TestCase				):
						_a							:   Union[str, Any] =   PegasusTokenizer
						_a							:   List[Any] =   PegasusTokenizerFast
						_a							:   Dict =   True
						_a							:   int =   True






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase           =	PegasusTokenizer(_A		,      offset=0		,      mask_token_sent=_A		,      mask_token="[MASK]"      )
											tokenizer.save_pretrained(self.tmpdirname      )






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"      )






						def     __SCREAMING_SNAKE_CASE(       self		,      **_A      ):




											"""simple docstring"""



											return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return ("This is a test", "This is a test")






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
											__lowerCAmelCase           =	self.tokenizer_class.from_pretrained(self.tmpdirname      )
											__lowerCAmelCase           =	(
											    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
											    " <pad> <pad> <pad>"
											)
											__lowerCAmelCase           =	rust_tokenizer([raw_input_str]		,      return_tensors=_A		,      add_special_tokens=_A      ).input_ids[0]
											__lowerCAmelCase           =	py_tokenizer([raw_input_str]		,      return_tensors=_A		,      add_special_tokens=_A      ).input_ids[0]
											self.assertListEqual(_A		,      _A      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	["This is going to be way too long." * 1_0_0_0, "short example"]
											__lowerCAmelCase           =	["not super long but more than 5 tokens", "tiny"]
											__lowerCAmelCase           =	self._large_tokenizer(_A		,      padding=_A		,      truncation=_A		,      return_tensors="pt"      )
											__lowerCAmelCase           =	self._large_tokenizer(
											    text_target=_A		,      max_length=5		,      padding=_A		,      truncation=_A		,      return_tensors="pt"      )

											assert batch.input_ids.shape == (2, 4_0_9_6)
											assert batch.attention_mask.shape == (2, 4_0_9_6)
											assert targets["input_ids"].shape == (2, 5)
											assert len(_A      ) == 2  # input_ids, attention_mask.






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	(
											    "This is an example string that is used to test the original TF implementation against the HF"
											    " implementation"
											)

											__lowerCAmelCase           =	self._large_tokenizer(_A      ).input_ids

											self.assertListEqual(
											    _A		,      [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]		,      )










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      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# 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      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

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

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

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

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      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__         =      parser.parse_args()

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




