'''simple docstring'''






import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   KandinskyVaaPriorPipeline
   __lowerCAmelCase         =   ["prompt"]
   __lowerCAmelCase         =   ["prompt", "negative_prompt"]
   __lowerCAmelCase         =   [
       "num_images_per_prompt",
       "generator",
       "num_inference_steps",
       "latents",
       "negative_prompt",
       "guidance_scale",
       "output_type",
       "return_dict",
   ]
   __lowerCAmelCase         =   False
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Tuple:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Any:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			str:
        return self.time_input_dim
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        return self.time_input_dim * 4
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Union[str, Any]:
        return 1_00
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[int]:
        _A    		=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
        return tokenizer
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        torch.manual_seed(0     )
        _A    		=  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=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=10_00, )
        return CLIPTextModelWithProjection(UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        torch.manual_seed(0     )

        _A    		=  {
            'num_attention_heads': 2,
            'attention_head_dim': 12,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        _A    		=  PriorTransformer(**UpperCamelCase__     )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        _A    		=  nn.Parameter(torch.ones(model.clip_std.shape     )     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Tuple:
        torch.manual_seed(0     )
        _A    		=  CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size, image_size=2_24, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=14, )

        _A    		=  CLIPVisionModelWithProjection(UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        _A    		=  CLIPImageProcessor(
            crop_size=2_24, do_center_crop=UpperCamelCase__, do_normalize=UpperCamelCase__, do_resize=UpperCamelCase__, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073], image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711], resample=3, size=2_24, )

        return image_processor
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[int]:
        _A    		=  self.dummy_prior
        _A    		=  self.dummy_image_encoder
        _A    		=  self.dummy_text_encoder
        _A    		=  self.dummy_tokenizer
        _A    		=  self.dummy_image_processor

        _A    		=  UnCLIPScheduler(
            variance_type='fixed_small_log', prediction_type='sample', num_train_timesteps=10_00, clip_sample=UpperCamelCase__, clip_sample_range=10.0, )

        _A    		=  {
            'prior': prior,
            'image_encoder': image_encoder,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'scheduler': scheduler,
            'image_processor': image_processor,
        }

        return components
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Tuple=0     )  ->			Optional[Any]:
        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
        _A    		=  {
            'prompt': 'horse',
            'generator': generator,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			str:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  pipe(**self.get_dummy_inputs(UpperCamelCase__     )     )
        _A    		=  output.image_embeds

        _A    		=  pipe(
            **self.get_dummy_inputs(UpperCamelCase__     ), return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -10:]
        _A    		=  image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _A    		=  np.array(
            [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Dict:
        _A    		=  torch_device == 'cpu'
        _A    		=  True
        _A    		=  False

        self._test_inference_batch_single_identical(
            test_max_difference=UpperCamelCase__, relax_max_difference=UpperCamelCase__, test_mean_pixel_difference=UpperCamelCase__, )

   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Union[str, Any]:
        _A    		=  torch_device == 'cpu'
        _A    		=  False

        self._test_attention_slicing_forward_pass(
            test_max_difference=UpperCamelCase__, test_mean_pixel_difference=UpperCamelCase__, )








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

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


UpperCAmelCase_		 =					logging.get_logger(__name__)


UpperCAmelCase_		 =					{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase_		 =					{
    """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"""
        ),
    },
}

UpperCAmelCase_		 =					"""</w>"""
UpperCAmelCase_		 =					"""@@ """

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       set()
   _A      =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      _A      =       char
   return pairs


# Speech2Text2 has no max input length
UpperCAmelCase_		 =					{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Dict =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[Any] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: List[Any] =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]="<s>"			,						__lowerCAmelCase					:					Tuple="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="</s>"			,						__lowerCAmelCase					:					Dict="<unk>"			,						__lowerCAmelCase					:					Union[str, Any]=False			,						__lowerCAmelCase					:					Optional[int]=None			,						**__lowerCAmelCase					:					str			,						)   ->	Dict:
    super().__init__(
        unk_token=__lowerCAmelCase			,						bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						do_lower_case=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

    _A      =       do_lower_case

    with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as vocab_handle:
       _A      =       json.load(__lowerCAmelCase      )
    _A      =       {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.'''      )

       _A      =       None
       _A      =       None
    else:
       with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as merges_handle:
          _A      =       merges_handle.read().split('''\n'''      )[:-1]

       _A      =       [tuple(merge.split()[:2]      ) for merge in merges]
       _A      =       dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase      )      )      )      )
       _A      =       {}

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	int:
    return len(self.decoder      )

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    return dict(self.encoder			,						**self.added_tokens_encoder      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
    if token in self.cache:
       return self.cache[token]
    _A      =       get_pairs(__lowerCAmelCase      )

    if not pairs:
       return token

    while True:
       _A      =       min(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : self.bpe_ranks.get(__lowerCAmelCase			,						float('''inf'''      )      )      )
       if bigram not in self.bpe_ranks:
          break
       _A  ,    _A      =       bigram
       _A      =       []
       _A      =       0
       while i < len(__lowerCAmelCase      ):
          try:
             _A      =       word.index(__lowerCAmelCase			,						__lowerCAmelCase      )
          except ValueError:
             new_word.extend(word[i:]      )
             break
          else:
             new_word.extend(word[i:j]      )
             _A      =       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
       _A      =       tuple(__lowerCAmelCase      )
       _A      =       new_word
       if len(__lowerCAmelCase      ) == 1:
          break
       else:
          _A      =       get_pairs(__lowerCAmelCase      )
    _A      =       ''' '''.join(__lowerCAmelCase      )
    if word == "\n  " + BPE_TOKEN_MERGES:
       _A      =       '''\n''' + BPE_TOKEN_MERGES

    if word.endswith(__lowerCAmelCase      ):
       _A      =       word.replace(__lowerCAmelCase			,						''''''      )

    _A      =       word.replace(''' '''			,						__lowerCAmelCase      )
    _A      =       word
    return word

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Optional[int]:

    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:
       _A      =       text.lower()

    _A      =       text.split()

    _A      =       []
    for token in text:
       if token:
          split_tokens.extend(list(self.bpe(__lowerCAmelCase      ).split(''' '''      )      )      )

    return split_tokens

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str      )   ->	int:
    return self.encoder.get(__lowerCAmelCase			,						self.encoder.get(self.unk_token      )      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					int      )   ->	str:
    _A      =       self.decoder.get(__lowerCAmelCase			,						self.unk_token      )
    return result

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[str]      )   ->	str:
    _A      =       ''' '''.join(__lowerCAmelCase      )

    # make sure @@ tokens are concatenated
    _A      =       ''''''.join(string.split(__lowerCAmelCase      )      )

    return string

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       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'''      )

    _A      =       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 __lowerCAmelCase      : 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!'''      )
             _A      =       token_index
          writer.write(''' '''.join(__lowerCAmelCase      ) + '''\n'''      )
          index += 1

    return (vocab_file, merges_file)



