


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

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


SCREAMING_SNAKE_CASE       :		Optional[Any]         =				logging.get_logger(__name__)


SCREAMING_SNAKE_CASE       :		Dict         =				{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE       :		int         =				{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

SCREAMING_SNAKE_CASE       :		Optional[Any]         =				'</w>'
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				'@@ '




def     UpperCamelCase    (      _a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :List[str]     =       set()
     lowercase_       :Any     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)		)
          lowercase_       :List[str]     =       char
     return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE       :		Dict         =				{'facebook/s2t-wav2vec2-large-en-de': 1_024}






class      UpperCamelCase					(		lowerCamelCase__     ):


   '''simple docstring'''







   lowercase					:	Optional[Any]									=VOCAB_FILES_NAMES
   lowercase					:	str									=PRETRAINED_VOCAB_FILES_MAP
   lowercase					:	List[Any]									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase					:	Union[str, Any]									=["""input_ids""", """attention_mask"""]
   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_="<s>"   ,							UpperCamelCase_="<pad>"   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="<unk>"   ,							UpperCamelCase_=False   ,							UpperCamelCase_=None   ,							**UpperCamelCase_   ,							):
        super().__init__(
            unk_token=__lowerCamelCase   ,							bos_token=__lowerCamelCase   ,							eos_token=__lowerCamelCase   ,							pad_token=__lowerCamelCase   ,							do_lower_case=__lowerCamelCase   ,							**__lowerCamelCase   ,							)

        lowercase_       :int     =       do_lower_case

        with open(__lowerCamelCase   ,							encoding='''utf-8'''		) as vocab_handle:
             lowercase_       :List[Any]     =       json.load(__lowerCamelCase		)
        lowercase_       :Dict     =       {v: k for k, v in self.encoder.items()}

        if merges_file is None:
             logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding."		)

             lowercase_       :Any     =       None
             lowercase_       :Tuple     =       None
        else:
             with open(__lowerCamelCase   ,							encoding='''utf-8'''		) as merges_handle:
                  lowercase_       :List[Any]     =       merges_handle.read().split('''\n'''		)[:-1]

             lowercase_       :int     =       [tuple(merge.split()[:2]		) for merge in merges]
             lowercase_       :str     =       dict(zip(__lowerCamelCase   ,							range(len(__lowerCamelCase		)		)		)		)
             lowercase_       :Optional[Any]     =       {}
   @property
   def 		UpperCamelCase  (     self		):
        return len(self.decoder		)
   def 		UpperCamelCase  (     self		):
        return dict(self.encoder   ,							**self.added_tokens_encoder		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Any     =       tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
        if token in self.cache:
             return self.cache[token]
        lowercase_       :Any     =       get_pairs(__lowerCamelCase		)

        if not pairs:
             return token

        while True:
             lowercase_       :Optional[int]     =       min(__lowerCamelCase   ,							key=lambda UpperCamelCase_		: self.bpe_ranks.get(__lowerCamelCase   ,							float('''inf'''		)		)		)
             if bigram not in self.bpe_ranks:
                  break
             lowercase_       :str     =       bigram
             lowercase_       :Union[str, Any]     =       []
             lowercase_       :Any     =       0
             while i < len(__lowerCamelCase		):
                  try:
                       lowercase_       :List[Any]     =       word.index(__lowerCamelCase   ,							__lowerCamelCase		)
                  except ValueError:
                       new_word.extend(word[i:]		)
                       break
                  else:
                       new_word.extend(word[i:j]		)
                       lowercase_       :Tuple     =       j

                  if word[i] == first and i < len(__lowerCamelCase		) - 1 and word[i + 1] == second:
                       new_word.append(first + second		)
                       i += 2
                  else:
                       new_word.append(word[i]		)
                       i += 1
             lowercase_       :int     =       tuple(__lowerCamelCase		)
             lowercase_       :Union[str, Any]     =       new_word
             if len(__lowerCamelCase		) == 1:
                  break
             else:
                  lowercase_       :List[str]     =       get_pairs(__lowerCamelCase		)
        lowercase_       :int     =       ''' '''.join(__lowerCamelCase		)
        if word == "\n  " + BPE_TOKEN_MERGES:
             lowercase_       :str     =       '''\n''' + BPE_TOKEN_MERGES

        if word.endswith(__lowerCamelCase		):
             lowercase_       :Optional[int]     =       word.replace(__lowerCamelCase   ,							''''''		)

        lowercase_       :int     =       word.replace(''' '''   ,							__lowerCamelCase		)
        lowercase_       :Any     =       word
        return word
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):






        if self.bpe_ranks is None:
             raise ValueError(
                 '''This tokenizer was instantiated without a `merges.txt` file, so'''
                 ''' that it can only be used for decoding, not for encoding.'''
                 '''Make sure to provide `merges.txt` file at instantiation to enable '''
                 '''encoding.'''		)

        if self.do_lower_case:
             lowercase_       :Union[str, Any]     =       text.lower()

        lowercase_       :Union[str, Any]     =       text.split()

        lowercase_       :List[Any]     =       []
        for token in text:
             if token:
                  split_tokens.extend(list(self.bpe(__lowerCamelCase		).split(''' '''		)		)		)

        return split_tokens
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return self.encoder.get(__lowerCamelCase   ,							self.encoder.get(self.unk_token		)		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Any     =       self.decoder.get(__lowerCamelCase   ,							self.unk_token		)
        return result
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :int     =       ''' '''.join(__lowerCamelCase		)

        # make sure @@ tokens are concatenated
        lowercase_       :int     =       ''''''.join(string.split(__lowerCamelCase		)		)

        return string




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):






        if not os.path.isdir(__lowerCamelCase		):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
             return
        lowercase_       :Tuple     =       os.path.join(
            __lowerCamelCase   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        lowercase_       :Optional[int]     =       os.path.join(
            __lowerCamelCase   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

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

        lowercase_       :Optional[int]     =       0
        if self.bpe_ranks is None:
             return (vocab_file,)

        with open(__lowerCamelCase   ,							'''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!'''		)
                       lowercase_       :List[str]     =       token_index
                  writer.write(''' '''.join(__lowerCamelCase		) + '''\n'''		)
                  index += 1

        return (vocab_file, merges_file)





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE       :		int         =				False






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 32
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :List[Any]     =       VQModel(
            block_out_channels=[32, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,							latent_channels=3   ,							num_vq_embeddings=self.num_embed   ,							vq_embed_dim=3   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
        return tokenizer
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :int     =       CLIPTextConfig(
            bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=1000   ,							)
        return CLIPTextModel(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)

        lowercase_       :int     =       12
        lowercase_       :List[Any]     =       12

        lowercase_       :Dict     =       {
            '''attention_bias''': True,
            '''cross_attention_dim''': 32,
            '''attention_head_dim''': height * width,
            '''num_attention_heads''': 1,
            '''num_vector_embeds''': self.num_embed,
            '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
            '''norm_num_groups''': 32,
            '''sample_size''': width,
            '''activation_fn''': '''geglu-approximate''',
        }

        lowercase_       :int     =       TransformeraDModel(**UpperCamelCase_		)
        return model
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''

        lowercase_       :int     =       self.dummy_vqvae
        lowercase_       :int     =       self.dummy_text_encoder
        lowercase_       :Any     =       self.dummy_tokenizer
        lowercase_       :Optional[int]     =       self.dummy_transformer
        lowercase_       :List[str]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :int     =       LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_		)

        lowercase_       :List[Any]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Union[str, Any]     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Dict     =       '''teddy bear playing in the pool'''

        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Any     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :str     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

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

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :str     =       np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

        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 		UpperCamelCase  (     self		):
        lowercase_       :int     =       '''cpu'''

        lowercase_       :Dict     =       self.dummy_vqvae
        lowercase_       :str     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       self.dummy_tokenizer
        lowercase_       :Any     =       self.dummy_transformer
        lowercase_       :Optional[Any]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :List[str]     =       LearnedClassifierFreeSamplingEmbeddings(
            learnable=UpperCamelCase_   ,							hidden_size=self.text_embedder_hidden_size   ,							length=tokenizer.model_max_length		)

        lowercase_       :Optional[int]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Dict     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :int     =       '''teddy bear playing in the pool'''

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Optional[Any]     =       output.images

        lowercase_       :Optional[int]     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Dict     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

        lowercase_       :List[str]     =       image[0, -3:, -3:, -1]
        lowercase_       :Optional[int]     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :Dict     =       np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2






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


   '''simple docstring'''







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




   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''		)

        lowercase_       :int     =       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''		)
        lowercase_       :Tuple     =       pipeline.to(UpperCamelCase_		)
        pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :int     =       pipeline(
            '''teddy bear playing in the pool'''   ,							num_images_per_prompt=1   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)

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

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image		).max() < 2.0

