

"""simple docstring"""


import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class _lowerCamelCase    :






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						int:

         """simple docstring"""
         return self.get_dummy_input()






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						List[str]:

         """simple docstring"""
         if self.block_type == "down":
               return (4, 32, 16, 16)
         elif self.block_type == "mid":
               return (4, 32, 32, 32)
         elif self.block_type == "up":
               return (4, 32, 64, 64)

         raise ValueError(f"""\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'."""   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Dict=True   ,     UpperCamelCase       :		List[str]=False   ,     UpperCamelCase       :		Optional[Any]=False   ,     UpperCamelCase       :		Any=False   ,     )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	4
         lowerCAmelCase__      :			List[Any]   =	32
         lowerCAmelCase__      :			Optional[Any]   =	(32, 32)

         lowerCAmelCase__      :			Tuple   =	torch.manual_seed(0   )
         lowerCAmelCase__      :			int   =	torch.device(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	(batch_size, num_channels) + sizes
         lowerCAmelCase__      :			Dict   =	randn_tensor(UpperCamelCase   ,     generator=UpperCamelCase   ,     device=UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	{"""hidden_states""": hidden_states}

         if include_temb:
               lowerCAmelCase__      :			Any   =	1_28
               lowerCAmelCase__      :			Optional[int]   =	randn_tensor((batch_size, temb_channels)   ,     generator=UpperCamelCase   ,     device=UpperCamelCase   )

         if include_res_hidden_states_tuple:
               lowerCAmelCase__      :			Optional[Any]   =	torch.manual_seed(1   )
               lowerCAmelCase__      :			int   =	(randn_tensor(UpperCamelCase   ,     generator=UpperCamelCase   ,     device=UpperCamelCase   ),)

         if include_encoder_hidden_states:
               lowerCAmelCase__      :			Union[str, Any]   =	floats_tensor((batch_size, 32, 32)   ).to(UpperCamelCase   )

         if include_skip_sample:
               lowerCAmelCase__      :			List[Any]   =	randn_tensor(((batch_size, 3) + sizes)   ,     generator=UpperCamelCase   ,     device=UpperCamelCase   )

         return dummy_input






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	{
             """in_channels""": 32,
             """out_channels""": 32,
             """temb_channels""": 1_28,
         }
         if self.block_type == "up":
               lowerCAmelCase__      :			Tuple   =	32

         if self.block_type == "mid":
               init_dict.pop("""out_channels"""   )

         lowerCAmelCase__      :			int   =	self.dummy_input
         return init_dict, inputs_dict






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	self.prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	self.block_class(**UpperCamelCase   )
         unet_block.to(UpperCamelCase   )
         unet_block.eval()

         with torch.no_grad():
               lowerCAmelCase__      :			str   =	unet_block(**UpperCamelCase   )

         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			Optional[int]   =	output[0]

         self.assertEqual(output.shape   ,     self.output_shape   )

         lowerCAmelCase__      :			List[str]   =	output[0, -1, -3:, -3:]
         lowerCAmelCase__      :			Union[str, Any]   =	torch.tensor(UpperCamelCase   ).to(UpperCamelCase   )
         assert torch_all_close(output_slice.flatten()   ,     UpperCamelCase   ,     atol=5E-3   )






   @unittest.skipIf(torch_device == """mps"""   ,     """Training is not supported in mps"""   )
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	self.prepare_init_args_and_inputs_for_common()
         lowerCAmelCase__      :			Optional[int]   =	self.block_class(**UpperCamelCase   )
         model.to(UpperCamelCase   )
         model.train()
         lowerCAmelCase__      :			Union[str, Any]   =	model(**UpperCamelCase   )

         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			int   =	output[0]

         lowerCAmelCase__      :			Any   =	torch.device(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	randn_tensor(output.shape   ,     device=UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	torch.nn.functional.mse_loss(UpperCamelCase   ,     UpperCamelCase   )
         loss.backward()











"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

_A													=      logging.get_logger(__name__)


_A													=      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_A													=      {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

_A													=      {"""facebook/blenderbot-3B""": 1_2_8}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	lowercase_				(			)						->    Tuple:
      lowerCAmelCase__      :			int   =	(
          list(range(ord("""!"""				)    ,		ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				)    ,		ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				)    ,		ord("""ÿ"""				) + 1				)				)
      )
      lowerCAmelCase__      :			Any   =	bs[:]
      lowerCAmelCase__      :			Optional[int]   =	0
      for b in range(2**8				):
            if b not in bs:
                  bs.append(__UpperCAmelCase				)
                  cs.append(2**8 + n				)
                  n += 1
      lowerCAmelCase__      :			Dict   =	[chr(__UpperCAmelCase				) for n in cs]
      return dict(zip(__UpperCAmelCase    ,		__UpperCAmelCase				)				)



def 	lowercase_				(			__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			List[Any]   =	set()
      lowerCAmelCase__      :			Union[str, Any]   =	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            lowerCAmelCase__      :			Optional[Any]   =	char
      return pairs


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :List[Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    :Optional[Any]     				=     ["input_ids", "attention_mask"]






   def __init__(		self       :		Any   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		Any="replace"   ,     UpperCamelCase       :		Optional[Any]="<s>"   ,     UpperCamelCase       :		Union[str, Any]="</s>"   ,     UpperCamelCase       :		Optional[int]="</s>"   ,     UpperCamelCase       :		str="<s>"   ,     UpperCamelCase       :		int="<unk>"   ,     UpperCamelCase       :		int="<pad>"   ,     UpperCamelCase       :		Dict="<mask>"   ,     UpperCamelCase       :		Optional[int]=False   ,     **UpperCamelCase       :		Optional[Any]   ,     )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else bos_token
         lowerCAmelCase__      :			int   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else eos_token
         lowerCAmelCase__      :			Dict   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else sep_token
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else cls_token
         lowerCAmelCase__      :			int   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else unk_token
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowerCAmelCase__      :			Union[str, Any]   =	AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,     UpperCamelCase   ) else mask_token

         super().__init__(
             errors=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     unk_token=UpperCamelCase   ,     sep_token=UpperCamelCase   ,     cls_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     mask_token=UpperCamelCase   ,     add_prefix_space=UpperCamelCase   ,     **UpperCamelCase   ,     )

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as vocab_handle:
               lowerCAmelCase__      :			Any   =	json.load(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{v: k for k, v in self.encoder.items()}
         lowerCAmelCase__      :			Dict   =	errors  # how to handle errors in decoding
         lowerCAmelCase__      :			Union[str, Any]   =	bytes_to_unicode()
         lowerCAmelCase__      :			List[str]   =	{v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as merges_handle:
               lowerCAmelCase__      :			Optional[int]   =	merges_handle.read().split("""\n"""   )[1:-1]
         lowerCAmelCase__      :			Dict   =	[tuple(merge.split()   ) for merge in bpe_merges]
         lowerCAmelCase__      :			Any   =	dict(zip(UpperCamelCase   ,     range(len(UpperCamelCase   )   )   )   )
         lowerCAmelCase__      :			Union[str, Any]   =	{}
         lowerCAmelCase__      :			Dict   =	add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowerCAmelCase__      :			Tuple   =	re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )






   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Union[str, Any]:

         """simple docstring"""
         return len(self.encoder   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         return dict(self.encoder   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		str   )			->						Union[str, Any]:

         """simple docstring"""
         if token in self.cache:
               return self.cache[token]
         lowerCAmelCase__      :			Union[str, Any]   =	tuple(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	get_pairs(UpperCamelCase   )

         if not pairs:
               return token

         while True:
               lowerCAmelCase__      :			List[str]   =	min(UpperCamelCase   ,     key=lambda UpperCamelCase   : self.bpe_ranks.get(UpperCamelCase   ,     float("""inf"""   )   )   )
               if bigram not in self.bpe_ranks:
                     break
               lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	bigram
               lowerCAmelCase__      :			List[str]   =	[]
               lowerCAmelCase__      :			List[str]   =	0
               while i < len(UpperCamelCase   ):
                     try:
                           lowerCAmelCase__      :			Optional[Any]   =	word.index(UpperCamelCase   ,     UpperCamelCase   )
                     except ValueError:
                           new_word.extend(word[i:]   )
                           break
                     else:
                           new_word.extend(word[i:j]   )
                           lowerCAmelCase__      :			List[str]   =	j

                     if word[i] == first and i < len(UpperCamelCase   ) - 1 and word[i + 1] == second:
                           new_word.append(first + second   )
                           i += 2
                     else:
                           new_word.append(word[i]   )
                           i += 1
               lowerCAmelCase__      :			List[Any]   =	tuple(UpperCamelCase   )
               lowerCAmelCase__      :			Tuple   =	new_word
               if len(UpperCamelCase   ) == 1:
                     break
               else:
                     lowerCAmelCase__      :			Any   =	get_pairs(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	""" """.join(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	word
         return word






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	[]
         for token in re.findall(self.pat   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			List[Any]   =	"""""".join(
                   self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase   ).split(""" """   )   )
         return bpe_tokens






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         return self.encoder.get(UpperCamelCase   ,     self.encoder.get(self.unk_token   )   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Optional[Any]   )			->						Tuple:

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	"""""".join(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""   ,     errors=self.errors   )
         return text






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
         lowerCAmelCase__      :			Union[str, Any]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
         lowerCAmelCase__      :			int   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as f:
               f.write(json.dumps(self.encoder   ,     indent=2   ,     sort_keys=UpperCamelCase   ,     ensure_ascii=UpperCamelCase   ) + """\n"""   )

         lowerCAmelCase__      :			Optional[Any]   =	0
         with open(UpperCamelCase   ,     """w"""   ,     encoding="""utf-8"""   ) as writer:
               writer.write("""#version: 0.2\n"""   )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,     key=lambda UpperCamelCase   : kv[1]   ):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                               """ Please check that the tokenizer is not corrupted!"""   )
                           lowerCAmelCase__      :			Dict   =	token_index
                     writer.write(""" """.join(UpperCamelCase   ) + """\n"""   )
                     index += 1

         return vocab_file, merge_file






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   ,     UpperCamelCase       :		bool = False   )			->						List[int]:

         """simple docstring"""
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=UpperCamelCase   ,     token_ids_a=UpperCamelCase   ,     already_has_special_tokens=UpperCamelCase   )

         if token_ids_a is None:
               return [1] + ([0] * len(UpperCamelCase   )) + [1]
         return [1] + ([0] * len(UpperCamelCase   )) + [1, 1] + ([0] * len(UpperCamelCase   )) + [1]






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						List[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	[self.sep_token_id]
         lowerCAmelCase__      :			List[str]   =	[self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep   ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Optional[int]=False   ,     **UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	kwargs.pop("""add_prefix_space"""   ,     self.add_prefix_space   )
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase   ) > 0 and not text[0].isspace()):
               lowerCAmelCase__      :			Tuple   =	""" """ + text
         return (text, kwargs)






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   )			->						Any:

         """simple docstring"""
         return token_ids_a + [self.eos_token_id]






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		"Conversation"   )			->						List[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	[]
         for is_user, text in conversation.iter_texts():
               if is_user:
                     # We need to space prefix as it's being done within blenderbot
                     inputs.append(""" """ + text   )
               else:
                     # Generated responses should contain them already.
                     inputs.append(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	"""  """.join(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	self.encode(UpperCamelCase   )
         if len(UpperCamelCase   ) > self.model_max_length:
               lowerCAmelCase__      :			List[str]   =	input_ids[-self.model_max_length :]
               logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""   )
         return input_ids





