


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp

@slow
@require_flax
class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									return f'''gaussian_noise_s={seed}_shape={"_".join([str(__A		) for s in shape]		)}.npy'''
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=(4, 4, 64, 64)     ,       _SCREAMING_SNAKE_CASE=False		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase											=     jnp.array(load_hf_numpy(self.get_file_format(__A     ,       __A		)		)     ,       dtype=__A		)
									return image
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE="CompVis/stable-diffusion-v1-4"		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase											=     'bf16' if fpaa else None

									_UpperCAmelCase						,			_UpperCAmelCase											=     FlaxUNetaDConditionModel.from_pretrained(
									    __A     ,       subfolder='unet'     ,       dtype=__A     ,       revision=__A		)
									return model, params
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=(4, 77, 768)     ,       _SCREAMING_SNAKE_CASE=False		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase											=     jnp.array(load_hf_numpy(self.get_file_format(__A     ,       __A		)		)     ,       dtype=__A		)
									return hidden_states
		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
		        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
		        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
		        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
		        # fmt: on
		    ]		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'     ,       fpaa=__A		)
									_UpperCAmelCase											=     self.get_latents(__A     ,       fpaa=__A		)
									_UpperCAmelCase											=     self.get_encoder_hidden_states(__A     ,       fpaa=__A		)

									_UpperCAmelCase											=     model.apply(
									    {'params': params}     ,       __A     ,       jnp.array(__A     ,       dtype=jnp.intaa		)     ,       encoder_hidden_states=__A     ,       ).sample

									assert sample.shape == latents.shape

									_UpperCAmelCase											=     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)     ,       dtype=jnp.floataa		)
									_UpperCAmelCase											=     jnp.array(__A     ,       dtype=jnp.floataa		)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(__A     ,       __A     ,       atol=1e-2		)






		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
		        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
		        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
		        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
		        # fmt: on
		    ]		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.get_unet_model(model_id='stabilityai/stable-diffusion-2'     ,       fpaa=__A		)
									_UpperCAmelCase											=     self.get_latents(__A     ,       shape=(4, 4, 96, 96)     ,       fpaa=__A		)
									_UpperCAmelCase											=     self.get_encoder_hidden_states(__A     ,       shape=(4, 77, 1024)     ,       fpaa=__A		)

									_UpperCAmelCase											=     model.apply(
									    {'params': params}     ,       __A     ,       jnp.array(__A     ,       dtype=jnp.intaa		)     ,       encoder_hidden_states=__A     ,       ).sample

									assert sample.shape == latents.shape

									_UpperCAmelCase											=     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)     ,       dtype=jnp.floataa		)
									_UpperCAmelCase											=     jnp.array(__A     ,       dtype=jnp.floataa		)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(__A     ,       __A     ,       atol=1e-2		)




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'▁'

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'prophetnet.tokenizer'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  a   (lowerCAmelCase__   ):
   __a					=  collections.OrderedDict()
   with open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""   ) as reader:
      __a					=  reader.readlines()
   for index, token in enumerate(lowerCAmelCase__   ):
      __a					=  token.rstrip("""\n"""   )
      __a					=  index
   return vocab


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[SEP]"  ,       __A="[UNK]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A = None  ,       **__A  ,       ):
        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__A  ,       eos_token=__A  ,       sep_token=__A  ,       unk_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__A  ,       )

        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__A    )    )
        __a					=  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        __a					=  {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10    ):
           __a					=  f'''[unused{i}]'''
           __a					=  5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        __a					=  12
        __a					=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(__A    )


     def __getstate__(	self    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d
        try:
           import sentencepiece as spm
        except ImportError:
           logger.warning(
               """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
               """ pip install sentencepiece"""    )
           raise

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""    ):
           __a					=  {}

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__A  ,       token_ids_a=__A  ,       already_has_special_tokens=__A    )

        if token_ids_a is None:
           return ([0] * len(__A    )) + [1]
        return ([0] * len(__A    )) + [1] + ([0] * len(__A    )) + [1]


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]

        if token_ids_a is None:
           return len(token_ids_a + sep    ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    ) + self.fairseq_offset


     def 	snake_case_  (	self    ):
        __a					=  {self.convert_ids_to_tokens(__A    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.encode(__A  ,       out_type=__A    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        __a					=  self.sp_model.PieceToId(__A    )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 	snake_case_  (	self  ,       __A    ):
        if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__A    ).replace(__A  ,       """ """    ).strip()
        return out_string


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

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ) and os.path.isfile(self.vocab_file    ):
           copyfile(self.vocab_file  ,       __A    )
        elif not os.path.isfile(self.vocab_file    ):
           with open(__A  ,       """wb"""    ) as fi:
              __a					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

        if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
        __a					=  [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep



