
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


SCREAMING_SNAKE_CASE				:int									=							get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	PegasusTokenizer
	snake_case_          =	PegasusTokenizerFast
	snake_case_          =	True
	snake_case_          =	True






	def 							UpperCamelCase_    (			self   :					Dict					):
					super().setUp()

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






	@cached_property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return PegasusTokenizer.from_pretrained("google/pegasus-large"					)






	def 							UpperCamelCase_    (			self   :					int				,**A   :					Dict					):
					return PegasusTokenizer.from_pretrained(self.tmpdirname				,**A					)






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					str					):
					return ("This is a test", "This is a test")






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					"</s>"
					__A       =					1

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






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					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					)				,11_03					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					self.assertEqual(self.get_tokenizer().vocab_size				,11_03					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
					__A       =					self.tokenizer_class.from_pretrained(self.tmpdirname					)
					__A       =					(
					    "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>"
					)
					__A       =					rust_tokenizer([raw_input_str]				,return_tensors=A				,add_special_tokens=A					).input_ids[0]
					__A       =					py_tokenizer([raw_input_str]				,return_tensors=A				,add_special_tokens=A					).input_ids[0]
					self.assertListEqual(A				,A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self._large_tokenizer
					# <mask_1> masks whole sentence while <mask_2> masks single word
					__A       =					"<mask_1> To ensure a <mask_2> flow of bank resolutions."
					__A       =					[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
					__A       =					tokenizer([raw_input_str]				,return_tensors=A					).input_ids[0]
					self.assertListEqual(A				,A					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self._large_tokenizer
					# The tracebacks for the following asserts are **better** without messages or self.assertEqual
					assert tokenizer.vocab_size == 9_61_03
					assert tokenizer.pad_token_id == 0
					assert tokenizer.eos_token_id == 1
					assert tokenizer.offset == 1_03
					assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
					assert tokenizer.unk_token == "<unk>"
					assert tokenizer.model_max_length == 10_24
					__A       =					"To ensure a smooth flow of bank resolutions."
					__A       =					[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
					__A       =					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 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					["This is going to be way too long." * 1_50, "short example"]
					__A       =					["not super long but more than 5 tokens", "tiny"]
					__A       =					self._large_tokenizer(A				,padding=A				,truncation=A				,return_tensors="pt"					)
					__A       =					self._large_tokenizer(
					    text_target=A				,max_length=5				,padding=A				,truncation=A				,return_tensors="pt"					)

					assert batch.input_ids.shape == (2, 10_24)
					assert batch.attention_mask.shape == (2, 10_24)
					assert targets["input_ids"].shape == (2, 5)
					assert len(A					) == 2  # input_ids, attention_mask.







	@slow
	def 							UpperCamelCase_    (			self   :					List[str]					):
					# fmt: off
					__A       =					{"input_ids": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	PegasusTokenizer
	snake_case_          =	PegasusTokenizerFast
	snake_case_          =	True
	snake_case_          =	True






	def 							UpperCamelCase_    (			self   :					Tuple					):
					super().setUp()

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






	@cached_property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"					)






	def 							UpperCamelCase_    (			self   :					str				,**A   :					Union[str, Any]					):
					return PegasusTokenizer.from_pretrained(self.tmpdirname				,**A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Dict					):
					return ("This is a test", "This is a test")






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






	@require_torch
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					["This is going to be way too long." * 10_00, "short example"]
					__A       =					["not super long but more than 5 tokens", "tiny"]
					__A       =					self._large_tokenizer(A				,padding=A				,truncation=A				,return_tensors="pt"					)
					__A       =					self._large_tokenizer(
					    text_target=A				,max_length=5				,padding=A				,truncation=A				,return_tensors="pt"					)

					assert batch.input_ids.shape == (2, 40_96)
					assert batch.attention_mask.shape == (2, 40_96)
					assert targets["input_ids"].shape == (2, 5)
					assert len(A					) == 2  # input_ids, attention_mask.







	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					(
					    "This is an example string that is used to test the original TF implementation against the HF"
					    " implementation"
					)

					__A       =					self._large_tokenizer(A					).input_ids

					self.assertListEqual(
					    A				,[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]				,)




'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''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(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


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

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   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''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						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												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



