





"""simple docstring"""




import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  UnCLIPPipeline
      lowercase_ :					Optional[Any]             =  TEXT_TO_IMAGE_PARAMS - {
          """negative_prompt""",
          """height""",
          """width""",
          """negative_prompt_embeds""",
          """guidance_scale""",
          """prompt_embeds""",
          """cross_attention_kwargs""",
      }
      lowercase_ :					Optional[int]             =  TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase_ :					Any             =  [
          """generator""",
          """return_dict""",
          """prior_num_inference_steps""",
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]
      lowercase_ :					Union[str, Any]             =  False

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim * 4

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1_0_0

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
        return tokenizer

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )
        A_    :			int									=    CLIPTextConfig(
            bos_token_id=0   , eos_token_id=2   , hidden_size=self.text_embedder_hidden_size   , projection_dim=self.text_embedder_hidden_size   , 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   , )
        return CLIPTextModelWithProjection(snake_case_    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			int									=    {
            'num_attention_heads': 2,
            'attention_head_dim': 1_2,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        A_    :			int									=    PriorTransformer(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Optional[Any]									=    {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        A_    :			Tuple									=    UnCLIPTextProjModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			str									=    {
            'sample_size': 3_2,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            '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,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        A_    :			Tuple									=    UNetaDConditionModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return {
            "sample_size": 6_4,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Union[str, Any]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(1    )

        A_    :			Optional[int]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.dummy_prior
        A_    :			Tuple									=    self.dummy_decoder
        A_    :			int									=    self.dummy_text_proj
        A_    :			Tuple									=    self.dummy_text_encoder
        A_    :			Optional[Any]									=    self.dummy_tokenizer
        A_    :			Any									=    self.dummy_super_res_first
        A_    :			Optional[Any]									=    self.dummy_super_res_last

        A_    :			Optional[int]									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='sample'   , num_train_timesteps=1_0_0_0   , clip_sample_range=5.0   , )

        A_    :			Tuple									=    UnCLIPScheduler(
            variance_type='learned_range'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			Any									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			int									=    {
            'prior': prior,
            'decoder': decoder,
            'text_proj': text_proj,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'super_res_first': super_res_first,
            'super_res_last': super_res_last,
            'prior_scheduler': prior_scheduler,
            'decoder_scheduler': decoder_scheduler,
            'super_res_scheduler': super_res_scheduler,
        }

        return components

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=0    ):

        """simple docstring"""

        if str(snake_case_    ).startswith('mps'    ):
          A_    :			Tuple									=    torch.manual_seed(snake_case_    )
        else:
          A_    :			Dict									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Optional[int]									=    {
            'prompt': 'horse',
            'generator': generator,
            'prior_num_inference_steps': 2,
            'decoder_num_inference_steps': 2,
            'super_res_num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    'cpu'

        A_    :			Optional[Any]									=    self.get_dummy_components()

        A_    :			Any									=    self.pipeline_class(**snake_case_    )
        A_    :			Dict									=    pipe.to(snake_case_    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    pipe(**self.get_dummy_inputs(snake_case_    )    )
        A_    :			int									=    output.images

        A_    :			Union[str, Any]									=    pipe(
            **self.get_dummy_inputs(snake_case_    )   , return_dict=snake_case_   , )[0]

        A_    :			Tuple									=    image[0, -3:, -3:, -1]
        A_    :			Any									=    image_from_tuple[0, -3:, -3:, -1]

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

        A_    :			Union[str, Any]									=    np.array(
            [
                0.99_97,
                0.99_88,
                0.00_28,
                0.99_97,
                0.99_84,
                0.99_65,
                0.00_29,
                0.99_86,
                0.00_25,
            ]    )

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    torch.device('cpu'    )

        class 					_UpperCAmelCase    :



              '''simple docstring'''
              lowercase_ :					Any             =  1


        A_    :			List[Any]									=    self.get_dummy_components()

        A_    :			List[str]									=    self.pipeline_class(**snake_case_    )
        A_    :			Any									=    pipe.to(snake_case_    )

        A_    :			int									=    components['prior']
        A_    :			Optional[int]									=    components['decoder']
        A_    :			Any									=    components['super_res_first']
        A_    :			List[Any]									=    components['tokenizer']
        A_    :			List[str]									=    components['text_encoder']

        A_    :			Any									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[Any]									=    prior.dtype
        A_    :			Dict									=    1

        A_    :			Any									=    (batch_size, prior.config.embedding_dim)
        A_    :			List[Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )
        A_    :			Tuple									=    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        A_    :			Any									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        A_    :			Any									=    (
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        A_    :			Union[str, Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			Union[str, Any]									=    'this is a prompt example'

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			str									=    pipe(
            [prompt]   , generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , output_type='np'   , )
        A_    :			Any									=    output.images

        A_    :			Any									=    tokenizer(
            snake_case_   , padding='max_length'   , max_length=tokenizer.model_max_length   , return_tensors='pt'   , )
        A_    :			List[str]									=    text_encoder(text_inputs.input_ids    )
        A_    :			List[str]									=    text_inputs.attention_mask

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[int]									=    pipe(
            generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , text_model_output=snake_case_   , text_attention_mask=snake_case_   , output_type='np'   , )[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text    ).max() < 1E-4

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=snake_case_   , expected_max_diff=0.01    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'
        A_    :			Optional[Any]									=    True
        A_    :			List[str]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=snake_case_   , relax_max_difference=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          A_    :			List[str]									=    [2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )
        else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=snake_case_    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_local()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_optional_components()


@nightly
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy'    )

        A_    :			str									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			Any									=    torch.manual_seed(0    )
        A_    :			str									=    pipeline(
            'horse'   , num_images_per_prompt=1   , generator=snake_case_   , output_type='np'   , )

        A_    :			int									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image    ).max() < 1E-1


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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy'    )

        A_    :			int									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			str									=    pipeline.to(snake_case_    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    torch.Generator(device='cpu'    ).manual_seed(0    )
        A_    :			Any									=    pipeline(
            'horse'   , generator=snake_case_   , output_type='np'   , )

        A_    :			List[str]									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        assert_mean_pixel_difference(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

        A_    :			List[Any]									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			Dict									=    pipe.to(snake_case_    )
        pipe.set_progress_bar_config(disable=snake_case_    )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        A_    :			Union[str, Any]									=    pipe(
            'horse'   , num_images_per_prompt=1   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , output_type='np'   , )

        A_    :			str									=    torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 1_0**9







import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase					:						Dict         =     logging.get_logger(__name__)

_UpperCAmelCase					:						int         =     {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_UpperCAmelCase					:						Optional[int]         =     {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_UpperCAmelCase					:						Optional[int]         =     {'stas/tiny-wmt19-en-de': 1024}
_UpperCAmelCase					:						List[Any]         =     {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1024,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict    ):
   lowerCamelCase									=set()
   lowerCamelCase									=word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)    )
      lowerCamelCase									=char
   return pairs






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[Any]    ):
   lowerCamelCase									=text.replace("""，"""			,""","""    )
   lowerCamelCase									=re.sub(R"""。\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""、"""			,""","""    )
   lowerCamelCase									=text.replace("""”"""			,"""\""""    )
   lowerCamelCase									=text.replace("""“"""			,"""\""""    )
   lowerCamelCase									=text.replace("""∶"""			,""":"""    )
   lowerCamelCase									=text.replace("""："""			,""":"""    )
   lowerCamelCase									=text.replace("""？"""			,"""?"""    )
   lowerCamelCase									=text.replace("""《"""			,"""\""""    )
   lowerCamelCase									=text.replace("""》"""			,"""\""""    )
   lowerCamelCase									=text.replace("""）"""			,""")"""    )
   lowerCamelCase									=text.replace("""！"""			,"""!"""    )
   lowerCamelCase									=text.replace("""（"""			,"""("""    )
   lowerCamelCase									=text.replace("""；"""			,""";"""    )
   lowerCamelCase									=text.replace("""１"""			,"""1"""    )
   lowerCamelCase									=text.replace("""」"""			,"""\""""    )
   lowerCamelCase									=text.replace("""「"""			,"""\""""    )
   lowerCamelCase									=text.replace("""０"""			,"""0"""    )
   lowerCamelCase									=text.replace("""３"""			,"""3"""    )
   lowerCamelCase									=text.replace("""２"""			,"""2"""    )
   lowerCamelCase									=text.replace("""５"""			,"""5"""    )
   lowerCamelCase									=text.replace("""６"""			,"""6"""    )
   lowerCamelCase									=text.replace("""９"""			,"""9"""    )
   lowerCamelCase									=text.replace("""７"""			,"""7"""    )
   lowerCamelCase									=text.replace("""８"""			,"""8"""    )
   lowerCamelCase									=text.replace("""４"""			,"""4"""    )
   lowerCamelCase									=re.sub(R"""．\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""～"""			,"""~"""    )
   lowerCamelCase									=text.replace("""’"""			,"""'"""    )
   lowerCamelCase									=text.replace("""…"""			,"""..."""    )
   lowerCamelCase									=text.replace("""━"""			,"""-"""    )
   lowerCamelCase									=text.replace("""〈"""			,"""<"""    )
   lowerCamelCase									=text.replace("""〉"""			,""">"""    )
   lowerCamelCase									=text.replace("""【"""			,"""["""    )
   lowerCamelCase									=text.replace("""】"""			,"""]"""    )
   lowerCamelCase									=text.replace("""％"""			,"""%"""    )
   return text






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									=[]
   for char in text:
      lowerCamelCase									=unicodedata.category(lowerCamelCase    )
      if cat.startswith("""C"""    ):
         continue
      output.append(lowerCamelCase    )
   return "".join(lowerCamelCase    )







class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Optional[int] 	=     VOCAB_FILES_NAMES
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_INIT_CONFIGURATION
     __lowerCamelCase       :    Tuple 	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase       :    Optional[int] 	=     ['''input_ids''', '''attention_mask''']





     def __init__(	self							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=False							, _UpperCAmelCase="<unk>"							, _UpperCAmelCase="<s>"							, _UpperCAmelCase="</s>"							, _UpperCAmelCase="<pad>"							, **_UpperCAmelCase							, )		->				Tuple:
        super().__init__(
            langs=_UpperCAmelCase							, src_vocab_file=_UpperCAmelCase							, tgt_vocab_file=_UpperCAmelCase							, merges_file=_UpperCAmelCase							, do_lower_case=_UpperCAmelCase							, unk_token=_UpperCAmelCase							, bos_token=_UpperCAmelCase							, sep_token=_UpperCAmelCase							, pad_token=_UpperCAmelCase							, **_UpperCAmelCase							, )

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses

        lowerCamelCase									=src_vocab_file
        lowerCamelCase									=tgt_vocab_file
        lowerCamelCase									=merges_file
        lowerCamelCase									=do_lower_case

        # cache of sm.MosesPunctNormalizer instance
        lowerCamelCase									={}
        # cache of sm.MosesTokenizer instance
        lowerCamelCase									={}
        lowerCamelCase									={}

        if langs and len(_UpperCAmelCase  ) == 2:
           lowerCamelCase				,       lowerCamelCase									=langs
        else:
           raise ValueError(
               F'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
               """Usually that means that tokenizer can't find a mapping for the given model path """
               """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""  )

        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as src_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as tgt_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
           lowerCamelCase									={v: k for k, v in tgt_vocab.items()}
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as merges_handle:
           lowerCamelCase									=merges_handle.read().split("""\n"""  )[:-1]
        lowerCamelCase									=[tuple(merge.split()[:2]  ) for merge in merges]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									={}





     def 						lowerCamelCase							(	self  )		->				Dict[str, int]:
        return self.get_src_vocab()





     @property
     def 						lowerCamelCase							(	self  )		->				int:
        return self.src_vocab_size





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				int:
        if lang not in self.cache_moses_punct_normalizer:
           lowerCamelCase									=self.sm.MosesPunctNormalizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=punct_normalizer
        return self.cache_moses_punct_normalizer[lang].normalize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        if lang not in self.cache_moses_tokenizer:
           lowerCamelCase									=self.sm.MosesTokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            _UpperCAmelCase							, aggressive_dash_splits=_UpperCAmelCase							, return_str=_UpperCAmelCase							, escape=_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Dict:
        if lang not in self.cache_moses_detokenizer:
           lowerCamelCase									=self.sm.MosesDetokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[str]:
        lowerCamelCase									=replace_unicode_punct(_UpperCAmelCase  )
        lowerCamelCase									=self.moses_punct_norm(_UpperCAmelCase							, _UpperCAmelCase  )
        lowerCamelCase									=remove_non_printing_char(_UpperCAmelCase  )
        return text





     @property
     def 						lowerCamelCase							(	self  )		->				Dict:
        return len(self.encoder  )





     @property
     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return len(self.decoder  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return dict(self.encoder							, **self.added_tokens_encoder  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return dict(self.decoder							, **self.added_tokens_decoder  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=tuple(token[:-1]  ) + (token[-1] + """</w>""",)
        if token in self.cache:
           return self.cache[token]
        lowerCamelCase									=get_pairs(_UpperCAmelCase  )

        if not pairs:
           return token + "</w>"

        while True:
           lowerCamelCase									=min(_UpperCAmelCase							, key=lambda _UpperCAmelCase  : self.bpe_ranks.get(_UpperCAmelCase							, float("""inf"""  )  )  )
           if bigram not in self.bpe_ranks:
              break
           lowerCamelCase				,       lowerCamelCase									=bigram
           lowerCamelCase									=[]
           lowerCamelCase									=0
           while i < len(_UpperCAmelCase  ):
              try:
                 lowerCamelCase									=word.index(_UpperCAmelCase							, _UpperCAmelCase  )
              except ValueError:
                 new_word.extend(word[i:]  )
                 break
              else:
                 new_word.extend(word[i:j]  )
                 lowerCamelCase									=j

              if word[i] == first and i < len(_UpperCAmelCase  ) - 1 and word[i + 1] == second:
                 new_word.append(first + second  )
                 i += 2
              else:
                 new_word.append(word[i]  )
                 i += 1
           lowerCamelCase									=tuple(_UpperCAmelCase  )
           lowerCamelCase									=new_word
           if len(_UpperCAmelCase  ) == 1:
              break
           else:
              lowerCamelCase									=get_pairs(_UpperCAmelCase  )
        lowerCamelCase									=""" """.join(_UpperCAmelCase  )
        if word == "\n  </w>":
           lowerCamelCase									="""\n</w>"""
        lowerCamelCase									=word
        return word





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase="en"							, _UpperCAmelCase=False  )		->				str:
        lowerCamelCase									=self.src_lang

        if self.do_lower_case:
           lowerCamelCase									=text.lower()

        if bypass_tokenizer:
           lowerCamelCase									=text.split()
        else:
           lowerCamelCase									=self.moses_pipeline(_UpperCAmelCase							, lang=_UpperCAmelCase  )
           lowerCamelCase									=self.moses_tokenize(_UpperCAmelCase							, lang=_UpperCAmelCase  )

        lowerCamelCase									=[]
        for token in text:
           if token:
              split_tokens.extend(list(self.bpe(_UpperCAmelCase  ).split(""" """  )  )  )

        return split_tokens





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[str]:
        return self.encoder.get(_UpperCAmelCase							, self.encoder.get(self.unk_token  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Tuple:
        return self.decoder.get(_UpperCAmelCase							, self.unk_token  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[Any]:
        lowerCamelCase									=[t.replace(""" """							, """"""  ).replace("""</w>"""							, """ """  ) for t in tokens]
        lowerCamelCase									="""""".join(_UpperCAmelCase  ).split()
        # detokenize
        lowerCamelCase									=self.moses_detokenize(_UpperCAmelCase							, self.tgt_lang  )
        return text





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return token_ids_a + sep
        return token_ids_a + sep + token_ids_a + sep





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None							, _UpperCAmelCase = False  )		->				List[int]:

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase							, token_ids_a=_UpperCAmelCase							, already_has_special_tokens=_UpperCAmelCase  )
        # no bos used in fairseq
        if token_ids_a is not None:
           return ([0] * len(_UpperCAmelCase  )) + [1] + ([0] * len(_UpperCAmelCase  )) + [1]
        return ([0] * len(_UpperCAmelCase  )) + [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return len(token_ids_a + sep  ) * [0]
        return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				Tuple[str]:
        if not os.path.isdir(_UpperCAmelCase  ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
           return

        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           f.write(json.dumps(self.encoder							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           lowerCamelCase									={v: k for k, v in self.decoder.items()}
           f.write(json.dumps(_UpperCAmelCase							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        lowerCamelCase									=0
        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							, key=lambda _UpperCAmelCase  : kv[1]  ):
              if index != token_index:
                 logger.warning(
                     F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                     """ Please check that the tokenizer is not corrupted!"""  )
                 lowerCamelCase									=token_index
              writer.write(""" """.join(_UpperCAmelCase  ) + """\n"""  )
              index += 1

        return src_vocab_file, tgt_vocab_file, merges_file





     def __getstate__(	self  )		->				List[Any]:
        lowerCamelCase									=self.__dict__.copy()
        lowerCamelCase									=None
        return state





     def __setstate__(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=d

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses