






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		"""▁"""

snake_case     					=		{"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case     					=		{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

snake_case     					=		{
    """facebook/mbart-large-50-one-to-many-mmt""": 1_024,
}

# fmt: off
snake_case     					=		["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[int]								=						VOCAB_FILES_NAMES
     UpperCamelCase_				:		List[Any]								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_				:		Union[str, Any]								=						PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_				:		List[Any]								=						['''input_ids''', '''attention_mask''']

     UpperCamelCase_				:		List[int]								=						[]
     UpperCamelCase_				:		List[int]								=						[]


     def __init__(					self							:						Any       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, Any]=None       ,			UpperCAmelCase_							:						Optional[Any]=None       ,			UpperCAmelCase_							:						Any="</s>"       ,			UpperCAmelCase_							:						Optional[int]="</s>"       ,			UpperCAmelCase_							:						str="<s>"       ,			UpperCAmelCase_							:						List[Any]="<unk>"       ,			UpperCAmelCase_							:						List[str]="<pad>"       ,			UpperCAmelCase_							:						Any="<mask>"       ,			UpperCAmelCase_							:						Optional[Dict[str, Any]] = None       ,			**UpperCAmelCase_							:						List[str]       ,			):
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE							:   Tuple						    =     AddedToken(UpperCAmelCase_       ,			lstrip=UpperCAmelCase_       ,			rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ) else mask_token

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {} if sp_model_kwargs is None else sp_model_kwargs

       SCREAMING_SNAKE_CASE							:   Dict						    =     kwargs.get("additional_special_tokens"       ,			[]   )
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=UpperCAmelCase_       ,			tgt_lang=UpperCAmelCase_       ,			eos_token=UpperCAmelCase_       ,			unk_token=UpperCAmelCase_       ,			sep_token=UpperCAmelCase_       ,			cls_token=UpperCAmelCase_       ,			pad_token=UpperCAmelCase_       ,			mask_token=UpperCAmelCase_       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   int						    =     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(UpperCAmelCase_   )   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     1

       SCREAMING_SNAKE_CASE							:   Any						    =     len(self.sp_model   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase_   )
       }
       SCREAMING_SNAKE_CASE							:   List[str]						    =     {v: k for k, v in self.lang_code_to_id.items()}
       SCREAMING_SNAKE_CASE							:   str						    =     len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}

       SCREAMING_SNAKE_CASE							:   List[str]						    =     src_lang if src_lang is not None else "en_XX"
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.lang_code_to_id[self._src_lang]
       SCREAMING_SNAKE_CASE							:   int						    =     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang   )


     @property
     def 					_A							(					self							:						Tuple   ):
       return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token


     @property
     def 					_A							(					self							:						List[Any]   ):
       return self._src_lang


     @src_lang.setter
     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang   )


     def __getstate__(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.__dict__.copy()
       SCREAMING_SNAKE_CASE							:   Dict						    =     None
       return state


     def __setstate__(					self							:						List[Any]       ,			UpperCAmelCase_							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   str						    =     d

       # for backward compatibility
       if not hasattr(self       ,			"sp_model_kwargs"   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     {}

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )


     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						str   ):
       return self.sp_model.encode(UpperCAmelCase_       ,			out_type=UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						str   ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.sp_model.PieceToId(UpperCAmelCase_   )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						int   ):
       if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset   )


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     []
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     ""
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(UpperCAmelCase_   ) + token
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     True
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     []
         else:
           current_sub_tokens.append(UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Tuple						    =     False
       out_string += self.sp_model.decode(UpperCAmelCase_   )
       return out_string.strip()


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str] = None   ):
       if not os.path.isdir(UpperCAmelCase_   ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
         return
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     os.path.join(
           UpperCAmelCase_       ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,			UpperCAmelCase_   )
       elif not os.path.isfile(self.vocab_file   ):
         with open(UpperCAmelCase_       ,			"wb"   ) as fi:
           SCREAMING_SNAKE_CASE							:   List[str]						    =     self.sp_model.serialized_model_proto()
           fi.write(UpperCAmelCase_   )

       return (out_vocab_file,)


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None       ,			UpperCAmelCase_							:						bool = False   ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase_       ,			token_ids_a=UpperCAmelCase_       ,			already_has_special_tokens=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Tuple						    =     [1] * len(self.prefix_tokens   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     [1] * len(self.suffix_tokens   )
       if token_ids_a is None:
         return prefix_ones + ([0] * len(UpperCAmelCase_   )) + suffix_ones
       return prefix_ones + ([0] * len(UpperCAmelCase_   )) + ([0] * len(UpperCAmelCase_   )) + suffix_ones


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None   ):
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str]       ,			UpperCAmelCase_							:						Optional[str]       ,			**UpperCAmelCase_							:						Any   ):
       if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     src_lang
       SCREAMING_SNAKE_CASE							:   str						    =     self(UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.convert_tokens_to_ids(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     tgt_lang_id
       return inputs


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						str = "en_XX"       ,			UpperCAmelCase_							:						Optional[List[str]] = None       ,			UpperCAmelCase_							:						str = "ro_RO"       ,			**UpperCAmelCase_							:						Union[str, Any]       ,			):
       SCREAMING_SNAKE_CASE							:   Dict						    =     src_lang
       SCREAMING_SNAKE_CASE							:   Dict						    =     tgt_lang
       return super().prepare_seqaseq_batch(UpperCAmelCase_       ,			UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[Any]   ):
       return self.set_src_lang_special_tokens(self.src_lang   )


     def 					_A							(					self							:						Dict   ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang   )


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						str   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.lang_code_to_id[src_lang]
       SCREAMING_SNAKE_CASE							:   Dict						    =     [self.cur_lang_code_id]
       SCREAMING_SNAKE_CASE							:   str						    =     [self.eos_token_id]






     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						str   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.lang_code_to_id[tgt_lang]
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.cur_lang_code_id]
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.eos_token_id]








'''simple docstring'''





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							PriorTransformer
 lowercase_					:    Dict				=							"hidden_states"







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							4
   UpperCAmelCase__      :					List[Any]				       =							8
   UpperCAmelCase__      :					Union[str, Any]				       =							7

   UpperCAmelCase__      :					Tuple				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					str				       =							4
   UpperCAmelCase__      :					int				       =							8
   UpperCAmelCase__      :					Optional[int]				       =							7

   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   return (4, 8)







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   return (4, 8)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   UpperCAmelCase__      :					Optional[Any]				       =							self.dummy_input
   return init_dict, inputs_dict







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"			,				output_loading_info=snake_case__ )
   self.assertIsNotNone(snake_case__ )
   self.assertEqual(len(loading_info["missing_keys"] )			,				0 )

   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							model(**self.dummy_input )[0]

   assert hidden_states is not None, "Make sure output is not None"







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__      :					Tuple				       =							self.model_class(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							inspect.signature(model.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__      :					int				       =							[*signature.parameters.keys()]

   UpperCAmelCase__      :					Optional[Any]				       =							["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
   UpperCAmelCase__      :					List[Any]				       =							model.to(snake_case__ )

   if hasattr(snake_case__			,				"set_default_attn_processor" ):
     model.set_default_attn_processor()

   UpperCAmelCase__      :					int				       =							self.get_dummy_seed_input()

   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )[0]

   UpperCAmelCase__      :					Any				       =							output[0, :5].flatten().cpu()
   print(snake_case__ )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   UpperCAmelCase__      :					Optional[int]				       =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
   self.assertTrue(torch_all_close(snake_case__			,				snake_case__			,				rtol=1e-2 ) )






@slow
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Any=7_68			,				snake_case__		:  List[Any]=77			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							batch_size
   UpperCAmelCase__      :					Any				       =							embedding_dim
   UpperCAmelCase__      :					Any				       =							num_embeddings

   UpperCAmelCase__      :					str				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ] )
 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior" )
   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							self.get_dummy_seed_input(seed=snake_case__ )

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**snake_case__ )[0]

   assert list(sample.shape ) == [1, 7_68]

   UpperCAmelCase__      :					List[str]				       =							sample[0, :8].flatten().cpu()
   print(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							torch.tensor(snake_case__ )

   assert torch_all_close(snake_case__			,				snake_case__			,				atol=1e-3 )



