



'''simple docstring'''



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_lowercase       :						Any						=None


_lowercase       :						Optional[int]						=logging.get_logger(__name__)


_lowercase       :						List[Any]						="▁"

_lowercase       :						Dict						={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase       :						Optional[int]						={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase       :						int						={
    "google/reformer-crime-and-punishment": 52_4288,
}






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  VOCAB_FILES_NAMES
			A__								=  PRETRAINED_VOCAB_FILES_MAP
			A__								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__								=  ['input_ids', 'attention_mask']
			A__								=  ReformerTokenizer



			def __init__(     self		:						int    ,			__UpperCamelCase		:						str=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Optional[int]="</s>"    ,			__UpperCamelCase		:						Dict="<unk>"    ,			__UpperCamelCase		:						Optional[int]=[]    ,			**__UpperCamelCase		:						int    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							super().__init__(
							    __UpperCamelCase    ,			tokenizer_file=__UpperCamelCase    ,			eos_token=__UpperCamelCase    ,			unk_token=__UpperCamelCase    ,			additional_special_tokens=__UpperCamelCase    ,			**__UpperCamelCase    ,			)

							snake_case__      :   List[Any]      =    vocab_file
							snake_case__      :   Union[str, Any]      =    False if not self.vocab_file else True







			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[str] = None			)      ->      Tuple[str]:
							"""simple docstring"""





							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''			)

							if not os.path.isdir(__UpperCamelCase			):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
											return
							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			):
											copyfile(self.vocab_file    ,			__UpperCamelCase			)

							return (out_vocab_file,)







'''simple docstring'''

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self,						lowercase_ = False )    ->							Union[str, Any]:
					snake_case							=					10

					if only_cross_attention:
						snake_case							=					12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						snake_case							=					query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Optional[int]:
					snake_case							=					2

					snake_case							=					torch.rand(lowercase_,						lowercase_,						3,						2 )
					snake_case							=					torch.rand(lowercase_,						4,						lowercase_ )
					snake_case							=					None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}






				def 					_lowerCamelCase    (       self )    ->							str:
					# self and cross attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is not None )
					self.assertTrue(attn.to_v is not None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					# only self attention

					torch.manual_seed(0 )

					snake_case							=					self.get_constructor_arguments(only_cross_attention=lowercase_ )
					snake_case							=					Attention(**lowercase_ )

					self.assertTrue(attn.to_k is None )
					self.assertTrue(attn.to_v is None )

					snake_case							=					self.get_forward_arguments(
					    query_dim=constructor_args['query_dim'],						added_kv_proj_dim=constructor_args['added_kv_proj_dim'] )

					snake_case							=					attn(**lowercase_ )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all() )







class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					# the initial conversion succeeds
					snake_case							=					DiffusionPipeline.from_pretrained(
					    'hf-internal-testing/tiny-stable-diffusion-pipe',						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(lowercase_ )

						# can also load the converted weights
						snake_case							=					DiffusionPipeline.from_pretrained(lowercase_,						device_map='sequential',						safety_checker=lowercase_ )

					snake_case							=					pipe(
					    'foo',						num_inference_steps=2,						generator=torch.Generator('cpu' ).manual_seed(0 ),						output_type='np',						).images

					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )
					self.assertTrue(np.allclose(lowercase_,						lowercase_,						atol=1E-5 ) )





