


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


SCREAMING_SNAKE_CASE__					:     str       =     logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__					:     str       =     {
    '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',
    },
}

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'allenai/led-base-16384': 16_384,
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    LEDTokenizer
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


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

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

       _UpperCAmelCase		:							Union[str, Any]							=     add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       _UpperCAmelCase		:							Dict							=     """post_processor"""
       _UpperCAmelCase		:							Optional[int]							=     getattr(self.backend_tokenizer							,					A_							,					A_    )
       if tokenizer_component_instance:
         _UpperCAmelCase		:							int							=     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:
           _UpperCAmelCase		:							Union[str, Any]							=     tuple(state["""sep"""]    )
         if "cls" in state:
           _UpperCAmelCase		:							str							=     tuple(state["""cls"""]    )

         _UpperCAmelCase		:							Union[str, Any]							=     False

         if state.get("""add_prefix_space"""							,					A_    ) != add_prefix_space:
           _UpperCAmelCase		:							Any							=     add_prefix_space
           _UpperCAmelCase		:							int							=     True

         if state.get("""trim_offsets"""							,					A_    ) != trim_offsets:
           _UpperCAmelCase		:							Optional[int]							=     trim_offsets
           _UpperCAmelCase		:							str							=     True

         if changes_to_apply:
           _UpperCAmelCase		:							Tuple							=     getattr(A_							,					state.pop("""type"""    )    )
           _UpperCAmelCase		:							Any							=     component_class(**A_    )
           setattr(self.backend_tokenizer							,					A_							,					A_    )


     @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							,					A_    ):
       _UpperCAmelCase		:							int							=     AddedToken(A_							,					lstrip=A_							,					rstrip=A_    ) if isinstance(A_							,					A_    ) else value
       _UpperCAmelCase		:							Any							=     value


     def        __snake_case(  self							,					*A_							,					**A_    ):
       _UpperCAmelCase		:							Dict							=     kwargs.get("""is_split_into_words"""							,					A_    )

       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(*A_							,					**A_    )


     def        __snake_case(  self							,					*A_							,					**A_    ):
       _UpperCAmelCase		:							Any							=     kwargs.get("""is_split_into_words"""							,					A_    )

       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(*A_							,					**A_    )


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     self._tokenizer.model.save(A_							,					name=A_    )
       return tuple(A_    )


     def        __snake_case(  self							,					A_							,					A_=None    ):
       _UpperCAmelCase		:							Any							=     [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							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Optional[int]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Optional[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							,					A_							,					A_ = None							,					A_ = PaddingStrategy.DO_NOT_PAD							,					A_ = None							,					A_ = None							,					):
       _UpperCAmelCase		:							Union[str, Any]							=     super()._pad(
           encoded_inputs=A_							,					max_length=A_							,					padding_strategy=A_							,					pad_to_multiple_of=A_							,					return_attention_mask=A_							,					)

       # Load from model defaults
       if return_attention_mask is None:
         _UpperCAmelCase		:							Optional[Any]							=     """attention_mask""" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
         _UpperCAmelCase		:							Any							=     encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         _UpperCAmelCase		:							Union[str, Any]							=     len(encoded_inputs["""global_attention_mask"""]    ) != len(A_    )

         if needs_to_be_padded:
           _UpperCAmelCase		:							Optional[int]							=     len(A_    ) - 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`
             _UpperCAmelCase		:							Any							=     (
                 encoded_inputs["""global_attention_mask"""] + [-1] * difference
             )
           elif self.padding_side == "left":
             _UpperCAmelCase		:							List[str]							=     [-1] * difference + encoded_inputs[
                 """global_attention_mask"""
             ]
           else:
             raise ValueError("""Invalid padding strategy:""" + str(self.padding_side    )    )

       return encoded_inputs









import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    IFPipeline
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_BATCH_PARAMS
     __SCREAMING_SNAKE_CASE     					=    PipelineTesterMixin.required_optional_params - {'''latents'''}


     def        __snake_case(  self    ):
       return self._get_dummy_components()


     def        __snake_case(  self							,					A_							,					A_=0    ):
       if str(A_    ).startswith("""mps"""    ):
         _UpperCAmelCase		:							Tuple							=     torch.manual_seed(A_    )
       else:
         _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device=A_    ).manual_seed(A_    )

       _UpperCAmelCase		:							Any							=     {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }

       return inputs


     def        __snake_case(  self    ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != """cuda"""							,					reason="""float16 requires CUDA"""    )
     def        __snake_case(  self    ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1    )


     def        __snake_case(  self    ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )


     def        __snake_case(  self    ):
       self._test_save_load_local()


     def        __snake_case(  self    ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2							,					)




     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()							,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""							,					)
     def        __snake_case(  self    ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


@slow
@require_torch_gpu
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


     def        __snake_case(  self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def        __snake_case(  self    ):
       # if

       _UpperCAmelCase		:							List[str]							=     IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""							,					variant="""fp16"""							,					torch_dtype=torch.floataa    )

       _UpperCAmelCase		:							Dict							=     IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""							,					variant="""fp16"""							,					torch_dtype=torch.floataa							,					text_encoder=A_							,					tokenizer=A_    )

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda"""    )

       _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     pipe_a.encode_prompt("""anime turtle"""							,					device="""cuda"""    )

       del pipe_a.tokenizer
       del pipe_a.text_encoder
       gc.collect()

       _UpperCAmelCase		:							List[Any]							=     None
       _UpperCAmelCase		:							Any							=     None

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       _UpperCAmelCase		:							Any							=     IFImgaImgPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, Any]							=     IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if_imgaimg(A_							,					A_							,					A_							,					A_    )

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       _UpperCAmelCase		:							Optional[int]							=     IFInpaintingPipeline(**pipe_a.components    )
       _UpperCAmelCase		:							Union[str, Any]							=     IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

       self._test_if_inpainting(A_							,					A_							,					A_							,					A_    )


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							Dict							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 13 * 10**9

       _UpperCAmelCase		:							str							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							List[Any]							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )


     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Dict							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							List[str]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							int							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							str							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _UpperCAmelCase		:							str							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[str]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Union[str, Any]							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[int]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Dict							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Any							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							int							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )




     def        __snake_case(  self							,					A_							,					A_							,					A_							,					A_    ):
       # pipeline 1

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							List[Any]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Dict							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Optional[Any]							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )
       _UpperCAmelCase		:							str							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					num_inference_steps=2							,					generator=A_							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Optional[Any]							=     output.images[0]

       assert image.shape == (64, 64, 3)

       _UpperCAmelCase		:							List[str]							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 10 * 10**9

       _UpperCAmelCase		:							Dict							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )

       # pipeline 2

       _start_torch_memory_measurement()

       _UpperCAmelCase		:							str							=     torch.Generator(device="""cpu"""    ).manual_seed(0    )

       _UpperCAmelCase		:							Optional[int]							=     floats_tensor((1, 3, 64, 64)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							Tuple							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(0    )    ).to(A_    )
       _UpperCAmelCase		:							str							=     floats_tensor((1, 3, 2_56, 2_56)							,					rng=random.Random(1    )    ).to(A_    )

       _UpperCAmelCase		:							Union[str, Any]							=     pipe_a(
           prompt_embeds=A_							,					negative_prompt_embeds=A_							,					image=A_							,					mask_image=A_							,					original_image=A_							,					generator=A_							,					num_inference_steps=2							,					output_type="""np"""							,					)

       _UpperCAmelCase		:							Any							=     output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       _UpperCAmelCase		:							Optional[int]							=     torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 10**9

       _UpperCAmelCase		:							Tuple							=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""    )
       assert_mean_pixel_difference(A_							,					A_    )




def    a__    (		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()





