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

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


if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
      _lowerCamelCase        =None


_lowerCamelCase        =logging.get_logger(__name__)

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

_lowerCamelCase        ={
    """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"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCamelCase        ={
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   str   	=      VOCAB_FILES_NAMES
      _UpperCAmelCase    :   Optional[Any]   	=      PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase    :   List[str]   	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase    :   Any   	=      ["""input_ids""", """attention_mask"""]
      _UpperCAmelCase    :   Optional[int]   	=      XLMRobertaTokenizer
      def __init__(				self   ,  __magic_name__=None   ,  __magic_name__=None   ,  __magic_name__="<s>"   ,  __magic_name__="</s>"   ,  __magic_name__="</s>"   ,  __magic_name__="<s>"   ,  __magic_name__="<unk>"   ,  __magic_name__="<pad>"   ,  __magic_name__="<mask>"   ,  **__magic_name__   ,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       lowerCamelCase		:     Dict          =				AddedToken(__magic_name__   ,  lstrip=__magic_name__   ,  rstrip=__magic_name__				) if isinstance(__magic_name__   ,  __magic_name__				) else mask_token

       super().__init__(
           __magic_name__   ,  tokenizer_file=__magic_name__   ,  bos_token=__magic_name__   ,  eos_token=__magic_name__   ,  sep_token=__magic_name__   ,  cls_token=__magic_name__   ,  unk_token=__magic_name__   ,  pad_token=__magic_name__   ,  mask_token=__magic_name__   ,  **__magic_name__   ,  )

       lowerCamelCase		:     List[Any]          =				vocab_file
       lowerCamelCase		:     List[Any]          =				False if not self.vocab_file else True
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowerCamelCase		:     List[str]          =				[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   ,  __magic_name__   ,  __magic_name__ = None				):
       lowerCamelCase		:     Optional[int]          =				[self.sep_token_id]
       lowerCamelCase		:     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 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = 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(__magic_name__				):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
        return
       lowerCamelCase		:     Any          =				os.path.join(
           __magic_name__   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

       return (out_vocab_file,)






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   Any   	=      KandinskyPipeline
      _UpperCAmelCase    :   Any   	=      [
          """prompt""",
          """image_embeds""",
          """negative_image_embeds""",
      ]
      _UpperCAmelCase    :   Union[str, Any]   	=      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
      _UpperCAmelCase    :   Dict   	=      [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _UpperCAmelCase    :   List[Any]   	=      False
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim * 4
      @property
      def 						UpperCamelCase__							(				self				):
       return 1_0_0
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
       return tokenizer
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Union[str, Any]          =				MCLIPConfig(
           numDims=self.cross_attention_dim   ,  transformerDimensions=self.text_embedder_hidden_size   ,  hidden_size=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  vocab_size=1_0_0_5   ,  )

       lowerCamelCase		:     Dict          =				MultilingualCLIP(__magic_name__				)
       lowerCamelCase		:     Optional[Any]          =				text_encoder.eval()

       return text_encoder
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)

       lowerCamelCase		:     Any          =				{
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_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""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       lowerCamelCase		:     Optional[int]          =				UNetaDConditionModel(**__magic_name__				)
       return model
      @property
      def 						UpperCamelCase__							(				self				):
       return {
           "block_out_channels": [3_2, 6_4],
           "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": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Dict          =				VQModel(**self.dummy_movq_kwargs				)
       return model
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.dummy_text_encoder
       lowerCamelCase		:     int          =				self.dummy_tokenizer
       lowerCamelCase		:     Optional[int]          =				self.dummy_unet
       lowerCamelCase		:     str          =				self.dummy_movq

       lowerCamelCase		:     Optional[int]          =				DDIMScheduler(
           num_train_timesteps=1_0_0_0   ,  beta_schedule="""linear"""   ,  beta_start=0.00_085   ,  beta_end=0.012   ,  clip_sample=__magic_name__   ,  set_alpha_to_one=__magic_name__   ,  steps_offset=1   ,  prediction_type="""epsilon"""   ,  thresholding=__magic_name__   ,  )

       lowerCamelCase		:     Any          =				{
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }
       return components
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
       lowerCamelCase		:     str          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(seed + 1				)				).to(__magic_name__				)
       if str(__magic_name__				).startswith("""mps"""				):
        lowerCamelCase		:     Union[str, Any]          =				torch.manual_seed(__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				{
           """prompt""": """horse""",
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 6_4,
           """width""": 6_4,
           """guidance_scale""": 4.0,
           """num_inference_steps""": 2,
           """output_type""": """np""",
       }
       return inputs






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				"""cpu"""

       lowerCamelCase		:     str          =				self.get_dummy_components()

       lowerCamelCase		:     Optional[Any]          =				self.pipeline_class(**__magic_name__				)
       lowerCamelCase		:     str          =				pipe.to(__magic_name__				)

       pipe.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     str          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
       lowerCamelCase		:     str          =				output.images

       lowerCamelCase		:     Dict          =				pipe(
           **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

       lowerCamelCase		:     int          =				image[0, -3:, -3:, -1]
       lowerCamelCase		:     Tuple          =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       lowerCamelCase		:     int          =				np.array(
           [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]				)

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






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_text2img_cat_fp16.npy"""				)

       lowerCamelCase		:     List[Any]          =				KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""   ,  torch_dtype=torch.floataa				)
       pipe_prior.to(__magic_name__				)

       lowerCamelCase		:     List[str]          =				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,  torch_dtype=torch.floataa				)
       lowerCamelCase		:     str          =				pipeline.to(__magic_name__				)
       pipeline.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     Dict          =				"""red cat, 4k photo"""

       lowerCamelCase		:     Tuple          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase      ,   lowerCamelCase		:     Any          =				pipe_prior(
           __magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=5   ,  negative_prompt=""""""   ,  ).to_tuple()

       lowerCamelCase		:     Dict          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase		:     Optional[Any]          =				pipeline(
           __magic_name__   ,  image_embeds=__magic_name__   ,  negative_image_embeds=__magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=1_0_0   ,  output_type="""np"""   ,  )

       lowerCamelCase		:     str          =				output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       assert_mean_pixel_difference(__magic_name__   ,  __magic_name__				)




