

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()






class 			UpperCAmelCase__						(			_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	unittest.TestCase			):






  """simple docstring"""


  a 	=   StableUnCLIPImgaImgPipeline
  a 	=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  a 	=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a 	=   frozenset(
      []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  a 	=   frozenset([]			)






  def  lowercase_						(				self		:						Dict   )						->				Union[str, Any]:
       SCREAMING_SNAKE_CASE__       =       32
       SCREAMING_SNAKE_CASE__       =       embedder_hidden_size

       # image encoding components

       SCREAMING_SNAKE_CASE__       =       CLIPImageProcessor(crop_size=32      ,  size=32   )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       CLIPVisionModelWithProjection(
           CLIPVisionConfig(
               hidden_size=lowercase__      ,  projection_dim=lowercase__      ,  num_hidden_layers=5      ,  num_attention_heads=4      ,  image_size=32      ,  intermediate_size=37      ,  patch_size=1      ,  )   )

       # regular denoising components

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       StableUnCLIPImageNormalizer(embedding_dim=lowercase__   )
       SCREAMING_SNAKE_CASE__       =       DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=lowercase__      ,  projection_dim=32      ,  intermediate_size=37      ,  layer_norm_eps=1e-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  )   )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       UNetaDConditionModel(
           sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')      ,  up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')      ,  block_out_channels=(32, 64)      ,  attention_head_dim=(2, 4)      ,  class_embed_type='''projection'''      ,  projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,  cross_attention_dim=lowercase__      ,  layers_per_block=1      ,  upcast_attention=lowercase__      ,  use_linear_projection=lowercase__      ,  )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       DDIMScheduler(
           beta_schedule='''scaled_linear'''      ,  beta_start=0.00085      ,  beta_end=0.012      ,  prediction_type='''v_prediction'''      ,  set_alpha_to_one=lowercase__      ,  steps_offset=1      ,  )

       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       AutoencoderKL()

       SCREAMING_SNAKE_CASE__       =       {
           # image encoding components
           '''feature_extractor''': feature_extractor,
           '''image_encoder''': image_encoder.eval(),
           # image noising components
           '''image_normalizer''': image_normalizer.eval(),
           '''image_noising_scheduler''': image_noising_scheduler,
           # regular denoising components
           '''tokenizer''': tokenizer,
           '''text_encoder''': text_encoder.eval(),
           '''unet''': unet.eval(),
           '''scheduler''': scheduler,
           '''vae''': vae.eval(),
       }

       return components






  def  lowercase_						(				self		:						Optional[Any]      ,  __lowerCamelCase		:						Dict      ,  __lowerCamelCase		:						Tuple=0      ,  __lowerCamelCase		:						int=True   )						->				Any:
       if str(lowercase__   ).startswith('''mps'''   ):
            SCREAMING_SNAKE_CASE__       =       torch.manual_seed(lowercase__   )
       else:
            SCREAMING_SNAKE_CASE__       =       torch.Generator(device=lowercase__   ).manual_seed(lowercase__   )

       SCREAMING_SNAKE_CASE__       =       floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(lowercase__   )   ).to(lowercase__   )

       if pil_image:
            SCREAMING_SNAKE_CASE__       =       input_image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__       =       input_image.clamp(0      ,  1   )
            SCREAMING_SNAKE_CASE__       =       input_image.cpu().permute(0      ,  2      ,  3      ,  1   ).float().numpy()
            SCREAMING_SNAKE_CASE__       =       DiffusionPipeline.numpy_to_pil(lowercase__   )[0]

       return {
           "prompt": "An anime racoon running a marathon",
           "image": input_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "np",
       }






  @skip_mps
  def  lowercase_						(				self		:						List[Any]   )						->				List[str]:
       SCREAMING_SNAKE_CASE__       =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       =       StableUnCLIPImgaImgPipeline(**lowercase__   )
       SCREAMING_SNAKE_CASE__       =       sd_pipe.to(lowercase__   )
       sd_pipe.set_progress_bar_config(disable=lowercase__   )

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(lowercase__   )
       inputs.update({'''image_embeds''': None}   )
       SCREAMING_SNAKE_CASE__       =       sd_pipe(**lowercase__   ).images
       SCREAMING_SNAKE_CASE__       =       image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__       =       np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3






  def  lowercase_						(				self		:						Dict   )						->				Union[str, Any]:
       SCREAMING_SNAKE_CASE__       =       torch_device in ['''cpu''', '''mps''']

       self._test_attention_slicing_forward_pass(test_max_difference=lowercase__   )






  def  lowercase_						(				self		:						Optional[Any]   )						->				Optional[int]:
       SCREAMING_SNAKE_CASE__       =       torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=lowercase__   )







  @unittest.skipIf(
      torch_device != '''cuda''' or not is_xformers_available()      ,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,  )
  def  lowercase_						(				self		:						int   )						->				Union[str, Any]:
       self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase__   )






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






  """simple docstring"""


  def  lowercase_						(				self		:						int   )						->				Tuple:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def  lowercase_						(				self		:						Optional[Any]   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__       =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

       SCREAMING_SNAKE_CASE__       =       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''   )

       SCREAMING_SNAKE_CASE__       =       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-l-img2img'''      ,  torch_dtype=torch.floataa   )
       pipe.to(lowercase__   )
       pipe.set_progress_bar_config(disable=lowercase__   )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__       =       torch.Generator(device='''cpu'''   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       pipe(lowercase__      ,  '''anime turle'''      ,  generator=lowercase__      ,  output_type='''np'''   )

       SCREAMING_SNAKE_CASE__       =       output.images[0]

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

       assert_mean_pixel_difference(lowercase__      ,  lowercase__   )






  def  lowercase_						(				self		:						Dict   )						->				Any:
       SCREAMING_SNAKE_CASE__       =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

       SCREAMING_SNAKE_CASE__       =       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''   )

       SCREAMING_SNAKE_CASE__       =       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-h-img2img'''      ,  torch_dtype=torch.floataa   )
       pipe.to(lowercase__   )
       pipe.set_progress_bar_config(disable=lowercase__   )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__       =       torch.Generator(device='''cpu'''   ).manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       pipe(lowercase__      ,  '''anime turle'''      ,  generator=lowercase__      ,  output_type='''np'''   )

       SCREAMING_SNAKE_CASE__       =       output.images[0]

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

       assert_mean_pixel_difference(lowercase__      ,  lowercase__   )







  def  lowercase_						(				self		:						str   )						->				Dict:
       SCREAMING_SNAKE_CASE__       =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''   )

       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE__       =       StableUnCLIPImgaImgPipeline.from_pretrained(
           '''fusing/stable-unclip-2-1-h-img2img'''      ,  torch_dtype=torch.floataa   )
       SCREAMING_SNAKE_CASE__       =       pipe.to(lowercase__   )
       pipe.set_progress_bar_config(disable=lowercase__   )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__       =       pipe(
           lowercase__      ,  '''anime turtle'''      ,  num_inference_steps=2      ,  output_type='''np'''      ,  )

       SCREAMING_SNAKE_CASE__       =       torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbart import MBartTokenizer
else:
				lowerCAmelCase       		=    None


lowerCAmelCase       		=    logging.get_logger(__name__)


lowerCAmelCase       		=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase       		=    {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase       		=    {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCAmelCase       		=    ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[str]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		str										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[int]										=						['''input_ids''', '''attention_mask''']
	_lowerCAmelCase     :		Optional[int]										=						MBartTokenizer

	_lowerCAmelCase     :		List[int]										=						[]
	_lowerCAmelCase     :		List[int]										=						[]


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

							super().__init__(
							    vocab_file=lowercase__				,     tokenizer_file=lowercase__				,     bos_token=lowercase__				,     eos_token=lowercase__				,     sep_token=lowercase__				,     cls_token=lowercase__				,     unk_token=lowercase__				,     pad_token=lowercase__				,     mask_token=lowercase__				,     src_lang=lowercase__				,     tgt_lang=lowercase__				,     additional_special_tokens=lowercase__				,     **lowercase__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	vocab_file
							__UpperCAmelCase			:							int	    =	False if not self.vocab_file else True

							__UpperCAmelCase			:							str	    =	FAIRSEQ_LANGUAGE_CODES.copy()

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens])

							self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens})
							__UpperCAmelCase			:							Union[str, Any]	    =	{
							    lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							__UpperCAmelCase			:							Any	    =	src_lang if src_lang is not None else '''en_XX'''
							__UpperCAmelCase			:							Optional[int]	    =	self.convert_tokens_to_ids(self._src_lang)
							__UpperCAmelCase			:							List[Any]	    =	tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)


	@property
	def  A(    self):
							return self._src_lang


	@src_lang.setter
	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Optional[int]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							int	    =	[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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__):
							if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
							__UpperCAmelCase			:							Optional[Any]	    =	src_lang
							__UpperCAmelCase			:							Union[str, Any]	    =	self(lowercase__				,     add_special_tokens=lowercase__				,     return_tensors=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							int	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	tgt_lang_id
							return inputs


	def  A(    self				,     lowercase__				,     lowercase__ = "en_XX"				,     lowercase__ = None				,     lowercase__ = "ro_RO"				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Any	    =	src_lang
							__UpperCAmelCase			:							Tuple	    =	tgt_lang
							return super().prepare_seqaseq_batch(lowercase__				,     lowercase__				,     **lowercase__)


	def  A(    self):
							return self.set_src_lang_special_tokens(self.src_lang)


	def  A(    self):
							return self.set_tgt_lang_special_tokens(self.tgt_lang)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[]
							__UpperCAmelCase			:							Dict	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							int	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Dict	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							str	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	[]
							__UpperCAmelCase			:							List[Any]	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							Optional[int]	    =	processors.TemplateProcessing(
							    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,     self.prefix_tokens + self.suffix_tokens))				,     )


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							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(lowercase__):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
													return
							__UpperCAmelCase			:							List[Any]	    =	os.path.join(
							    lowercase__				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

							if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase__):
													copyfile(self.vocab_file				,     lowercase__)

							return (out_vocab_file,)

