
'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			CycleDiffusionPipeline
			lowerCamelCase			:		Optional[Any]	      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    "negative_prompt",
			    "height",
			    "width",
			    "negative_prompt_embeds",
			}
			lowerCamelCase			:		str	      =			PipelineTesterMixin.required_optional_params - {"latents"}
			lowerCamelCase			:		Dict	      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}    )
			lowerCamelCase			:		Union[str, Any]	      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase			:		Tuple	      =			IMAGE_TO_IMAGE_IMAGE_PARAMS

			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Union[str, Any]     =		UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)			,   layers_per_block=2			,   sample_size=3_2			,   in_channels=4			,   out_channels=4			,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,   cross_attention_dim=3_2			,   )
								lowerCamelCase_					:      int     =		DDIMScheduler(
								    beta_start=0.0_00_85			,   beta_end=0.0_12			,   beta_schedule='''scaled_linear'''			,   num_train_timesteps=1_0_0_0			,   clip_sample=A			,   set_alpha_to_one=A			,   )
								torch.manual_seed(0      )
								lowerCamelCase_					:      Dict     =		AutoencoderKL(
								    block_out_channels=[3_2, 6_4]			,   in_channels=3			,   out_channels=3			,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,   latent_channels=4			,   )
								torch.manual_seed(0      )
								lowerCamelCase_					:      str     =		CLIPTextConfig(
								    bos_token_id=0			,   eos_token_id=2			,   hidden_size=3_2			,   intermediate_size=3_7			,   layer_norm_eps=1E-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   pad_token_id=1			,   vocab_size=1_0_0_0			,   )
								lowerCamelCase_					:      Union[str, Any]     =		CLIPTextModel(A      )
								lowerCamelCase_					:      Union[str, Any]     =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

								lowerCamelCase_					:      int     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 3_2, 3_2)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[Any]     =		image / 2 + 0.5
								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      Optional[Any]     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      List[str]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[int]     =		{
								    '''prompt''': '''An astronaut riding an elephant''',
								    '''source_prompt''': '''An astronaut riding a horse''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''eta''': 0.1,
								    '''strength''': 0.8,
								    '''guidance_scale''': 3,
								    '''source_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								lowerCamelCase_					:      List[Any]     =		self.get_dummy_components()
								lowerCamelCase_					:      Optional[Any]     =		CycleDiffusionPipeline(**A      )
								lowerCamelCase_					:      Optional[int]     =		pipe.to(A      )
								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		self.get_dummy_inputs(A      )
								lowerCamelCase_					:      Optional[int]     =		pipe(**A      )
								lowerCamelCase_					:      Dict     =		output.images

								lowerCamelCase_					:      str     =		images[0, -3:, -3:, -1]

								assert images.shape == (1, 3_2, 3_2, 3)
								lowerCamelCase_					:      int     =		np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

			@unittest.skipIf(torch_device != '''cuda'''			,   '''This test requires a GPU'''      )
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		self.get_dummy_components()
								for name, module in components.items():
													if hasattr(A			,   '''half'''      ):
																		lowerCamelCase_					:      str     =		module.half()
								lowerCamelCase_					:      Tuple     =		CycleDiffusionPipeline(**A      )
								lowerCamelCase_					:      Optional[int]     =		pipe.to(A      )
								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      List[str]     =		self.get_dummy_inputs(A      )
								lowerCamelCase_					:      Optional[int]     =		pipe(**A      )
								lowerCamelCase_					:      Dict     =		output.images

								lowerCamelCase_					:      int     =		images[0, -3:, -3:, -1]

								assert images.shape == (1, 3_2, 3_2, 3)
								lowerCamelCase_					:      List[str]     =		np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

			@skip_mps
			def        UpperCAmelCase__ (self      ):
								return super().test_save_load_local()

			@unittest.skip('''non-deterministic pipeline'''      )
			def        UpperCAmelCase__ (self      ):
								return super().test_inference_batch_single_identical()

			@skip_mps
			def        UpperCAmelCase__ (self      ):
								return super().test_dict_tuple_outputs_equivalent()

			@skip_mps
			def        UpperCAmelCase__ (self      ):
								return super().test_save_load_optional_components()






			@skip_mps
			def        UpperCAmelCase__ (self      ):
								return super().test_attention_slicing_forward_pass()

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

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''      )
								lowerCamelCase_					:      Dict     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''      )
								lowerCamelCase_					:      Optional[Any]     =		init_image.resize((5_1_2, 5_1_2)      )

								lowerCamelCase_					:      int     =		'''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase_					:      str     =		DDIMScheduler.from_pretrained(A			,   subfolder='''scheduler'''      )
								lowerCamelCase_					:      Optional[Any]     =		CycleDiffusionPipeline.from_pretrained(
								    A			,   scheduler=A			,   safety_checker=A			,   torch_dtype=torch.floataa			,   revision='''fp16'''      )

								pipe.to(A      )
								pipe.set_progress_bar_config(disable=A      )
								pipe.enable_attention_slicing()

								lowerCamelCase_					:      Any     =		'''A black colored car'''
								lowerCamelCase_					:      List[Any]     =		'''A blue colored car'''

								lowerCamelCase_					:      Any     =		torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[int]     =		pipe(
								    prompt=A			,   source_prompt=A			,   image=A			,   num_inference_steps=1_0_0			,   eta=0.1			,   strength=0.85			,   guidance_scale=3			,   source_guidance_scale=1			,   generator=A			,   output_type='''np'''			,   )
								lowerCamelCase_					:      Tuple     =		output.images

								# the values aren't exactly equal, but the images look the same visually
								assert np.abs(image - expected_image      ).max() < 5E-1






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''      )
								lowerCamelCase_					:      int     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''      )
								lowerCamelCase_					:      Dict     =		init_image.resize((5_1_2, 5_1_2)      )

								lowerCamelCase_					:      List[Any]     =		'''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase_					:      str     =		DDIMScheduler.from_pretrained(A			,   subfolder='''scheduler'''      )
								lowerCamelCase_					:      Optional[Any]     =		CycleDiffusionPipeline.from_pretrained(A			,   scheduler=A			,   safety_checker=A      )

								pipe.to(A      )
								pipe.set_progress_bar_config(disable=A      )
								pipe.enable_attention_slicing()

								lowerCamelCase_					:      int     =		'''A black colored car'''
								lowerCamelCase_					:      Tuple     =		'''A blue colored car'''

								lowerCamelCase_					:      Any     =		torch.manual_seed(0      )
								lowerCamelCase_					:      List[Any]     =		pipe(
								    prompt=A			,   source_prompt=A			,   image=A			,   num_inference_steps=1_0_0			,   eta=0.1			,   strength=0.85			,   guidance_scale=3			,   source_guidance_scale=1			,   generator=A			,   output_type='''np'''			,   )
								lowerCamelCase_					:      List[str]     =		output.images

								assert np.abs(image - expected_image      ).max() < 2E-2




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowercase							:   Any					=     logging.get_logger(__name__)

__lowercase							:   List[str]					=     '''▁'''

__lowercase							:   Optional[Any]					=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase							:   List[str]					=     {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__lowercase							:   Union[str, Any]					=     {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Any	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		Optional[int]	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		str	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Union[str, Any]	      =			["input_ids", "attention_mask"]

			def __init__(self			,   A			,   A="<s>"			,   A="</s>"			,   A="</s>"			,   A="<s>"			,   A="<unk>"			,   A="<pad>"			,   A="<mask>"			,   A = None			,   **A			,   ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase_					:      Tuple     =		AddedToken(A			,   lstrip=A			,   rstrip=A      ) if isinstance(A			,   A      ) else mask_token

								lowerCamelCase_					:      Dict     =		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=A			,   eos_token=A			,   unk_token=A			,   sep_token=A			,   cls_token=A			,   pad_token=A			,   mask_token=A			,   sp_model_kwargs=self.sp_model_kwargs			,   **A			,   )

								lowerCamelCase_					:      Any     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(A      )      )
								lowerCamelCase_					:      Tuple     =		vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								lowerCamelCase_					:      str     =		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								lowerCamelCase_					:      int     =		1

								lowerCamelCase_					:      Union[str, Any]     =		len(self.sp_model      ) + self.fairseq_offset
								lowerCamelCase_					:      Any     =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(self      ):
								lowerCamelCase_					:      Any     =		self.__dict__.copy()
								lowerCamelCase_					:      Optional[Any]     =		None
								lowerCamelCase_					:      int     =		self.sp_model.serialized_model_proto()
								return state

			def __setstate__(self			,   A      ):
								lowerCamelCase_					:      Any     =		d

								# for backward compatibility
								if not hasattr(self			,   '''sp_model_kwargs'''      ):
													lowerCamelCase_					:      str     =		{}

								lowerCamelCase_					:      List[Any]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):

								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase_					:      Optional[int]     =		[self.cls_token_id]
								lowerCamelCase_					:      Optional[int]     =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

			def        UpperCAmelCase__ (self			,   A			,   A = None			,   A = False      ):

								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=A			,   token_ids_a=A			,   already_has_special_tokens=A      )

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

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      List[str]     =		[self.sep_token_id]
								lowerCamelCase_					:      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]

			@property
			def        UpperCAmelCase__ (self      ):
								return len(self.sp_model      ) + self.fairseq_offset + 1  # Add the <mask> token

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		{self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab

			def        UpperCAmelCase__ (self			,   A      ):
								return self.sp_model.encode(A			,   out_type=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								lowerCamelCase_					:      int     =		self.sp_model.PieceToId(A      )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

			def        UpperCAmelCase__ (self			,   A      ):
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		''''''.join(A      ).replace(A			,   ''' '''      ).strip()
								return out_string






			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								if not os.path.isdir(A      ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
													return
								lowerCamelCase_					:      str     =		os.path.join(
								    A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file			,   A      )
								elif not os.path.isfile(self.vocab_file      ):
													with open(A			,   '''wb'''      ) as fi:
																		lowerCamelCase_					:      Optional[Any]     =		self.sp_model.serialized_model_proto()
																		fi.write(A      )

								return (out_vocab_file,)


