import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class   lowercase      (       a			,    unittest.TestCase       ):
							lowercase__  :    Dict									=							KandinskyVaaImgaImgPipeline
							lowercase__  :    Optional[Any]									=							["""image_embeds""", """negative_image_embeds""", """image"""]
							lowercase__  :    Union[str, Any]									=							[
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							]
							lowercase__  :    int									=							[
							    """generator""",
							    """height""",
							    """width""",
							    """strength""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							lowercase__  :    Any									=							False




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												return 32




							@property
							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												return 32




							@property
							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												return self.time_input_dim




							@property
							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

												return self.time_input_dim * 4




							@property
							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												return 100




							@property
							def 		__snake_case(  self				:   List[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												torch.manual_seed(0 )

												SCREAMING_SNAKE_CASE           =    {
												    "in_channels": 4,
												    # Out channels is double in channels because predicts mean and variance
												    "out_channels": 8,
												    "addition_embed_type": "image",
												    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
												    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
												    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "layers_per_block": 1,
												    "encoder_hid_dim": self.text_embedder_hidden_size,
												    "encoder_hid_dim_type": "image_proj",
												    "cross_attention_dim": self.cross_attention_dim,
												    "attention_head_dim": 4,
												    "resnet_time_scale_shift": "scale_shift",
												    "class_embed_type": None,
												}

												SCREAMING_SNAKE_CASE           =    UNetaDConditionModel(**_UpperCamelCase )
												return model




							@property
							def 		__snake_case(  self				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return {
												    "block_out_channels": [32, 64],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




							@property
							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    VQModel(**self.dummy_movq_kwargs )
												return model




							def 		__snake_case(  self				:   List[Any] )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.dummy_unet
												SCREAMING_SNAKE_CASE           =    self.dummy_movq

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_schedule": "linear",
												    "beta_start": 0.0_0_0_8_5,
												    "beta_end": 0.0_1_2,
												    "clip_sample": False,
												    "set_alpha_to_one": False,
												    "steps_offset": 0,
												    "prediction_type": "epsilon",
												    "thresholding": False,
												}

												SCREAMING_SNAKE_CASE           =    DDIMScheduler(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    {
												    "unet": unet,
												    "scheduler": scheduler,
												    "movq": movq,
												}

												return components




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Optional[Any]=0 )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    floats_tensor((1, self.text_embedder_hidden_size)		,							rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    floats_tensor((1, self.text_embedder_hidden_size)		,							rng=random.Random(seed + 1 ) ).to(
												    _UpperCamelCase )
												# create init_image
												SCREAMING_SNAKE_CASE           =    floats_tensor((1, 3, 64, 64)		,							rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    image.cpu().permute(0		,							2		,							3		,							1 )[0]
												SCREAMING_SNAKE_CASE           =    Image.fromarray(np.uinta(_UpperCamelCase ) ).convert("RGB" ).resize((256, 256) )

												if str(_UpperCamelCase ).startswith("mps" ):
																	SCREAMING_SNAKE_CASE           =    torch.manual_seed(_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE           =    torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    {
												    "image": init_image,
												    "image_embeds": image_embeds,
												    "negative_image_embeds": negative_image_embeds,
												    "generator": generator,
												    "height": 64,
												    "width": 64,
												    "num_inference_steps": 10,
												    "guidance_scale": 7.0,
												    "strength": 0.2,
												    "output_type": "np",
												}
												return inputs





							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "cpu"

												SCREAMING_SNAKE_CASE           =    self.get_dummy_components()

												SCREAMING_SNAKE_CASE           =    self.pipeline_class(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    pipe.to(_UpperCamelCase )

												pipe.set_progress_bar_config(disable=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    pipe(**self.get_dummy_inputs(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    output.images

												SCREAMING_SNAKE_CASE           =    pipe(
												    **self.get_dummy_inputs(_UpperCamelCase )		,							return_dict=_UpperCamelCase		,							)[0]

												SCREAMING_SNAKE_CASE           =    image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE           =    image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												SCREAMING_SNAKE_CASE           =    np.array(
												    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
												assert (
												    np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
												), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
												), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



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




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

												SCREAMING_SNAKE_CASE           =    load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
												SCREAMING_SNAKE_CASE           =    "A red cartoon frog, 4k"

												SCREAMING_SNAKE_CASE           =    KandinskyVaaPriorPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-prior"		,							torch_dtype=torch.floataa )
												pipe_prior.to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    KandinskyVaaImgaImgPipeline.from_pretrained(
												    "kandinsky-community/kandinsky-2-2-decoder"		,							torch_dtype=torch.floataa )
												SCREAMING_SNAKE_CASE           =    pipeline.to(_UpperCamelCase )

												pipeline.set_progress_bar_config(disable=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.Generator(device="cpu" ).manual_seed(0 )
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    pipe_prior(
												    _UpperCamelCase		,							generator=_UpperCamelCase		,							num_inference_steps=5		,							negative_prompt=""		,							).to_tuple()

												SCREAMING_SNAKE_CASE           =    pipeline(
												    image=_UpperCamelCase		,							image_embeds=_UpperCamelCase		,							negative_image_embeds=_UpperCamelCase		,							generator=_UpperCamelCase		,							num_inference_steps=100		,							height=768		,							width=768		,							strength=0.2		,							output_type="np"		,							)

												SCREAMING_SNAKE_CASE           =    output.images[0]

												assert image.shape == (768, 768, 3)

												assert_mean_pixel_difference(_UpperCamelCase		,							_UpperCamelCase )





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


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


_lowerCamelCase   :				str     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''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''',
    },
}

_lowerCamelCase   :				List[Any]     			=						{
    '''allenai/led-base-16384''': 1_63_84,
}


class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							VOCAB_FILES_NAMES
							lowercase__  :    Any									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Optional[Any]									=							LEDTokenizer
							lowercase__  :    str									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[str]="replace"		,							_UpperCamelCase				:   str="<s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[str]="<s>"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   List[Any]="<pad>"		,							_UpperCamelCase				:   Tuple="<mask>"		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   List[Any]=True		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			Tuple:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
												if pre_tok_state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							pre_tok_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    add_prefix_space
																	SCREAMING_SNAKE_CASE           =    pre_tok_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												SCREAMING_SNAKE_CASE           =    "post_processor"
												SCREAMING_SNAKE_CASE           =    getattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )
												if tokenizer_component_instance:
																	SCREAMING_SNAKE_CASE           =    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:
																						SCREAMING_SNAKE_CASE           =    tuple(state["sep"] )
																	if "cls" in state:
																						SCREAMING_SNAKE_CASE           =    tuple(state["cls"] )

																	SCREAMING_SNAKE_CASE           =    False

																	if state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																						SCREAMING_SNAKE_CASE           =    add_prefix_space
																						SCREAMING_SNAKE_CASE           =    True

																	if state.get("trim_offsets"		,							_UpperCamelCase ) != trim_offsets:
																						SCREAMING_SNAKE_CASE           =    trim_offsets
																						SCREAMING_SNAKE_CASE           =    True

																	if changes_to_apply:
																						SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							state.pop("type" ) )
																						SCREAMING_SNAKE_CASE           =    component_class(**_UpperCamelCase )
																						setattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )




							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												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				:   Optional[int]		,							_UpperCamelCase				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase		,							_UpperCamelCase ) else value
												SCREAMING_SNAKE_CASE           =    value




							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Tuple )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [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				:   Dict		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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				:   Optional[Any]		,							_UpperCamelCase				:   Union[Dict[str, EncodedInput], BatchEncoding]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[bool] = None		,							)   ->			dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    super()._pad(
												    encoded_inputs=_UpperCamelCase		,							max_length=_UpperCamelCase		,							padding_strategy=_UpperCamelCase		,							pad_to_multiple_of=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							)

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

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

																	if needs_to_be_padded:
																						SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase ) - 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`
																											SCREAMING_SNAKE_CASE           =    (
																											    encoded_inputs["global_attention_mask"] + [-1] * difference
																											)
																						elif self.padding_side == "left":
																											SCREAMING_SNAKE_CASE           =    [-1] * difference + encoded_inputs[
																											    "global_attention_mask"
																											]
																						else:
																											raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

												return encoded_inputs




