
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


a_											=						logging.get_logger(__name__)


a_											=						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

a_											=						{
    "allenai/led-base-16384": 1_6384,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						LEDTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_="replace" ,       lowercase_="<s>" ,       lowercase_="</s>" ,       lowercase_="</s>" ,       lowercase_="<s>" ,       lowercase_="<unk>" ,       lowercase_="<pad>" ,       lowercase_="<mask>" ,       lowercase_=False ,       lowercase_=True ,       **lowercase_ ,       ):
     super().__init__(
         lowercase_ ,       lowercase_ ,       tokenizer_file=lowercase_ ,       errors=lowercase_ ,       bos_token=lowercase_ ,       eos_token=lowercase_ ,       sep_token=lowercase_ ,       cls_token=lowercase_ ,       unk_token=lowercase_ ,       pad_token=lowercase_ ,       mask_token=lowercase_ ,       add_prefix_space=lowercase_ ,       trim_offsets=lowercase_ ,       **lowercase_ ,       )

     snake_case_ :    str     			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
     if pre_tok_state.get("add_prefix_space" ,       lowercase_) != add_prefix_space:
         snake_case_ :    str     			=						getattr(lowercase_ ,       pre_tok_state.pop("type"))
         snake_case_ :    int     			=						add_prefix_space
         snake_case_ :    List[Any]     			=						pre_tok_class(**lowercase_)

     snake_case_ :    List[str]     			=						add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     snake_case_ :    str     			=						"post_processor"
     snake_case_ :    Tuple     			=						getattr(self.backend_tokenizer ,       lowercase_ ,       lowercase_)
     if tokenizer_component_instance:
         snake_case_ :    Dict     			=						json.loads(tokenizer_component_instance.__getstate__())

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
             snake_case_ :    str     			=						tuple(state["sep"])
         if "cls" in state:
             snake_case_ :    Optional[Any]     			=						tuple(state["cls"])

         snake_case_ :    Tuple     			=						False

         if state.get("add_prefix_space" ,       lowercase_) != add_prefix_space:
             snake_case_ :    int     			=						add_prefix_space
             snake_case_ :    int     			=						True

         if state.get("trim_offsets" ,       lowercase_) != trim_offsets:
             snake_case_ :    Optional[Any]     			=						trim_offsets
             snake_case_ :    Dict     			=						True

         if changes_to_apply:
             snake_case_ :    Tuple     			=						getattr(lowercase_ ,       state.pop("type"))
             snake_case_ :    int     			=						component_class(**lowercase_)
             setattr(self.backend_tokenizer ,       lowercase_ ,       lowercase_)





 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def        snake_case__		(    self):
     if self._mask_token is None:
         if self.verbose:
             logger.error("Using mask_token, but it is not set yet.")
         return None
     return str(self._mask_token)





 @mask_token.setter
 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Tuple     			=						AddedToken(lowercase_ ,       lstrip=lowercase_ ,       rstrip=lowercase_) if isinstance(lowercase_ ,       lowercase_) else value
     snake_case_ :    Tuple     			=						value





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Union[str, Any]     			=						kwargs.get("is_split_into_words" ,       lowercase_)

     if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs.")

     return super()._batch_encode_plus(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Optional[Any]     			=						kwargs.get("is_split_into_words" ,       lowercase_)

     if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs.")

     return super()._encode_plus(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    Any     			=						self._tokenizer.model.save(lowercase_ ,       name=lowercase_)
     return tuple(lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     snake_case_ :    str     			=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
         return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    int     			=						[self.sep_token_id]
     snake_case_ :    List[Any]     			=						[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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = PaddingStrategy.DO_NOT_PAD ,       lowercase_ = None ,       lowercase_ = None ,       ):
     snake_case_ :    str     			=						super()._pad(
         encoded_inputs=lowercase_ ,       max_length=lowercase_ ,       padding_strategy=lowercase_ ,       pad_to_multiple_of=lowercase_ ,       return_attention_mask=lowercase_ ,       )

     # Load from model defaults
     if return_attention_mask is None:
         snake_case_ :    str     			=						"attention_mask" in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
         snake_case_ :    Tuple     			=						encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         snake_case_ :    List[str]     			=						len(encoded_inputs["global_attention_mask"]) != len(lowercase_)

         if needs_to_be_padded:
             snake_case_ :    Optional[int]     			=						len(lowercase_) - len(encoded_inputs["global_attention_mask"])

             if self.padding_side == "right":
                 # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                 snake_case_ :    Union[str, Any]     			=						(
                     encoded_inputs["global_attention_mask"] + [-1] * difference
                 )
             elif self.padding_side == "left":
                 snake_case_ :    List[str]     			=						[-1] * difference + encoded_inputs[
                     "global_attention_mask"
                 ]
             else:
                 raise ValueError("Invalid padding strategy:" + str(self.padding_side))

     return encoded_inputs






'''simple docstring'''
import gc
import math
import unittest

import torch

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

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_											=						logging.get_logger(__name__)

enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						4
     snake_case_ :    int     			=						3
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						{
         "block_out_channels": (32, 64),
         "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
         "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
         "attention_head_dim": 3,
         "out_channels": 3,
         "in_channels": 3,
         "layers_per_block": 2,
         "sample_size": 32,
     }
     snake_case_ :    List[Any]     			=						self.dummy_input
     return init_dict, inputs_dict






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Dict     			=						4
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    Dict     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
         "layers_per_block": 2,
         "block_out_channels": (32, 64),
         "attention_head_dim": 32,
         "down_block_types": ("DownBlock2D", "DownBlock2D"),
         "up_block_types": ("UpBlock2D", "UpBlock2D"),
     }
     snake_case_ :    Any     			=						self.dummy_input
     return init_dict, inputs_dict





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)

     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    int     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model.to(lowercase_)
     snake_case_ :    str     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     snake_case_			, snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model_accelerate.to(lowercase_)
     model_accelerate.eval()

     snake_case_ :    Optional[int]     			=						torch.randn(
         1 ,       model_accelerate.config.in_channels ,       model_accelerate.config.sample_size ,       model_accelerate.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     snake_case_ :    Optional[int]     			=						model_accelerate(lowercase_ ,       lowercase_)["sample"]

     # two models don't need to stay in the device at the same time
     del model_accelerate
     torch.cuda.empty_cache()
     gc.collect()

     snake_case_			, snake_case_ :    Union[str, Any]     			=						UNetaDModel.from_pretrained(
         "fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_ ,       low_cpu_mem_usage=lowercase_)
     model_normal_load.to(lowercase_)
     model_normal_load.eval()
     snake_case_ :    Optional[Any]     			=						model_normal_load(lowercase_ ,       lowercase_)["sample"]

     assert torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
     model.eval()
     model.to(lowercase_)

     snake_case_ :    List[str]     			=						torch.randn(
         1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    str     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Tuple     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    Optional[int]     			=						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3))






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self ,       lowercase_=(32, 32)):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Dict     			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa ,       device=lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						{
         "block_out_channels": [32, 64, 64, 64],
         "in_channels": 3,
         "layers_per_block": 1,
         "out_channels": 3,
         "time_embedding_type": "fourier",
         "norm_eps": 1E-6,
         "mid_block_scale_factor": math.sqrt(2.0),
         "norm_num_groups": None,
         "down_block_types": [
             "SkipDownBlock2D",
             "AttnSkipDownBlock2D",
             "SkipDownBlock2D",
             "SkipDownBlock2D",
         ],
         "up_block_types": [
             "SkipUpBlock2D",
             "SkipUpBlock2D",
             "AttnSkipUpBlock2D",
             "SkipUpBlock2D",
         ],
     }
     snake_case_ :    Optional[Any]     			=						self.dummy_input
     return init_dict, inputs_dict





 @slow
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[int]     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,       output_loading_info=lowercase_)
     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    Dict     			=						self.dummy_input
     snake_case_ :    Tuple     			=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowercase_)
     snake_case_ :    Tuple     			=						noise
     snake_case_ :    Tuple     			=						model(**lowercase_)

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





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
     model.to(lowercase_)

     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    Dict     			=						(2_56, 2_56)

     snake_case_ :    Tuple     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    int     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[str]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
     model.to(lowercase_)

     snake_case_ :    Dict     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    List[Any]     			=						(32, 32)

     snake_case_ :    int     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     # not required for this model
     pass




