




import json
import os
from typing import Optional, Tuple

import regex as re

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


_lowerCamelCase	:		Tuple							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Optional[Any]							       =   {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_lowerCamelCase	:		Union[str, Any]							       =   {
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

_lowerCamelCase	:		Optional[Any]							       =   {
    """ctrl""": 256,
}

_lowerCamelCase	:		Tuple							       =   {
    """Pregnancy""": 168629,
    """Christianity""": 7675,
    """Explain""": 106423,
    """Fitness""": 63440,
    """Saving""": 63163,
    """Ask""": 27171,
    """Ass""": 95985,
    """Joke""": 163509,
    """Questions""": 45622,
    """Thoughts""": 49605,
    """Retail""": 52342,
    """Feminism""": 164338,
    """Writing""": 11992,
    """Atheism""": 192263,
    """Netflix""": 48616,
    """Computing""": 39639,
    """Opinion""": 43213,
    """Alone""": 44967,
    """Funny""": 58917,
    """Gaming""": 40358,
    """Human""": 4088,
    """India""": 1331,
    """Joker""": 77138,
    """Diet""": 36206,
    """Legal""": 11859,
    """Norman""": 4939,
    """Tip""": 72689,
    """Weight""": 52343,
    """Movies""": 46273,
    """Running""": 23425,
    """Science""": 2090,
    """Horror""": 37793,
    """Confession""": 60572,
    """Finance""": 12250,
    """Politics""": 16360,
    """Scary""": 191985,
    """Support""": 12654,
    """Technologies""": 32516,
    """Teenage""": 66160,
    """Event""": 32769,
    """Learned""": 67460,
    """Notion""": 182770,
    """Wikipedia""": 37583,
    """Books""": 6665,
    """Extract""": 76050,
    """Confessions""": 102701,
    """Conspiracy""": 75932,
    """Links""": 63674,
    """Narcissus""": 150425,
    """Relationship""": 54766,
    """Relationships""": 134796,
    """Reviews""": 41671,
    """News""": 4256,
    """Translation""": 26820,
    """multilingual""": 128406,
}







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    set()
       A__       =    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              A__       =    char

       A__       =    set(lowercase_						)
       return pairs


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      CONTROL_CODES


    def __init__(			self      :	str					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	Union[str, Any]="<unk>"					, **UpperCAmelCase__      :	List[Any])						->List[str]:





           '''simple docstring'''

           super().__init__(unk_token=UpperCAmelCase__					, **UpperCAmelCase__)

           with open(UpperCAmelCase__					, encoding='''utf-8''') as vocab_handle:
                  A__       =    json.load(UpperCAmelCase__)
           A__       =    {v: k for k, v in self.encoder.items()}
           with open(UpperCAmelCase__					, encoding='''utf-8''') as merges_handle:
                  A__       =    merges_handle.read().split('''\n''')[1:-1]
           A__       =    [tuple(merge.split()) for merge in merges]
           A__       =    dict(zip(UpperCAmelCase__					, range(len(UpperCAmelCase__))))
           A__       =    {}


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Union[str, Any]:





           '''simple docstring'''

           return len(self.encoder)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->Dict:





           '''simple docstring'''

           return dict(self.encoder					, **self.added_tokens_encoder)


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	List[Any])						->Optional[Any]:





           '''simple docstring'''

           if token in self.cache:
                  return self.cache[token]
           A__       =    tuple(UpperCAmelCase__)
           A__       =    tuple(list(word[:-1]) + [word[-1] + '''</w>'''])
           A__       =    get_pairs(UpperCAmelCase__)

           if not pairs:
                  return token

           while True:
                  A__       =    min(UpperCAmelCase__					, key=lambda UpperCAmelCase__: self.bpe_ranks.get(UpperCAmelCase__					, float('''inf''')))
                  if bigram not in self.bpe_ranks:
                         break
                  A__ ,							A__       =    bigram
                  A__       =    []
                  A__       =    0
                  while i < len(UpperCAmelCase__):
                         try:
                                A__       =    word.index(UpperCAmelCase__					, UpperCAmelCase__)
                         except ValueError:
                                new_word.extend(word[i:])
                                break
                         else:
                                new_word.extend(word[i:j])
                                A__       =    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
                  A__       =    tuple(UpperCAmelCase__)
                  A__       =    new_word
                  if len(UpperCAmelCase__) == 1:
                         break
                  else:
                         A__       =    get_pairs(UpperCAmelCase__)
           A__       =    '''@@ '''.join(UpperCAmelCase__)
           A__       =    word[:-4]
           A__       =    word
           return word


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	Any)						->Optional[int]:





           '''simple docstring'''

           A__       =    []

           A__       =    re.findall(R'''\S+\n?'''					, UpperCAmelCase__)

           for token in words:
                  split_tokens.extend(list(self.bpe(UpperCAmelCase__).split(''' ''')))
           return split_tokens


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str])						->Any:





           '''simple docstring'''

           return self.encoder.get(UpperCAmelCase__					, self.encoder.get(self.unk_token))


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	str)						->int:





           '''simple docstring'''

           return self.decoder.get(UpperCAmelCase__					, self.unk_token)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    ''' '''.join(UpperCAmelCase__).replace('''@@ '''					, '''''').strip()
           return out_string





    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

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

           A__       =    0
           with open(UpperCAmelCase__					, '''w'''					, encoding='''utf-8''') as writer:
                  writer.write('''#version: 0.2\n''')
                  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 {merge_file}: BPE merge indices are not consecutive."""
                                    ''' Please check that the tokenizer is not corrupted!''')
                                A__       =    token_index
                         writer.write(''' '''.join(UpperCAmelCase__) + '''\n''')
                         index += 1

           return vocab_file, merge_file

    # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
    #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
    #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
    #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
    #     return ''.join(tokens_generated_so_far)



'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







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







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





