





'''simple docstring'''



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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       return F'''gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase	) for s in shape]	)}.npy'''

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

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   lowerCamelCase=(4, 4, 64, 64)						,   lowerCamelCase=False	):
       _snake_case     		=				jnp.bfloataa if fpaa else jnp.floataa
       _snake_case     		=				jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase						,   lowerCamelCase	)	)						,   dtype=lowerCamelCase	)
       return image

     def 	UpperCamelCase(					self						,   lowerCamelCase=False						,   lowerCamelCase="CompVis/stable-diffusion-v1-4"	):
       _snake_case     		=				jnp.bfloataa if fpaa else jnp.floataa
       _snake_case     		=				"bf16" if fpaa else None

       _snake_case		,		_snake_case     		=				FlaxUNetaDConditionModel.from_pretrained(
           lowerCamelCase						,   subfolder="unet"						,   dtype=lowerCamelCase						,   revision=lowerCamelCase	)
       return model, params

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   lowerCamelCase=(4, 77, 768)						,   lowerCamelCase=False	):
       _snake_case     		=				jnp.bfloataa if fpaa else jnp.floataa
       _snake_case     		=				jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase						,   lowerCamelCase	)	)						,   dtype=lowerCamelCase	)
       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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
             # fmt: on
         ]	)
     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case		,		_snake_case     		=				self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"						,   fpaa=lowerCamelCase	)
       _snake_case     		=				self.get_latents(lowerCamelCase						,   fpaa=lowerCamelCase	)
       _snake_case     		=				self.get_encoder_hidden_states(lowerCamelCase						,   fpaa=lowerCamelCase	)

       _snake_case     		=				model.apply(
           {"params": params}						,   lowerCamelCase						,   jnp.array(lowerCamelCase						,   dtype=jnp.intaa	)						,   encoder_hidden_states=lowerCamelCase						,   ).sample

       assert sample.shape == latents.shape

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

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(lowerCamelCase						,   lowerCamelCase						,   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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
             # fmt: on
         ]	)
     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case		,		_snake_case     		=				self.get_unet_model(model_id="stabilityai/stable-diffusion-2"						,   fpaa=lowerCamelCase	)
       _snake_case     		=				self.get_latents(lowerCamelCase						,   shape=(4, 4, 96, 96)						,   fpaa=lowerCamelCase	)
       _snake_case     		=				self.get_encoder_hidden_states(lowerCamelCase						,   shape=(4, 77, 1_024)						,   fpaa=lowerCamelCase	)

       _snake_case     		=				model.apply(
           {"params": params}						,   lowerCamelCase						,   jnp.array(lowerCamelCase						,   dtype=jnp.intaa	)						,   encoder_hidden_states=lowerCamelCase						,   ).sample

       assert sample.shape == latents.shape

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

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









'''simple docstring'''



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

import sentencepiece as spm

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


__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)

__magic_name__   :						str    =			"""▁"""

__magic_name__   :						int    =			{"""vocab_file""": """sentencepiece.bpe.model"""}

__magic_name__   :						List[str]    =			{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

__magic_name__   :						Union[str, Any]    =			{
    """facebook/xglm-564M""": 2_048,
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						str      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Any      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						Any      =   ['''input_ids''', '''attention_mask''']

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				{} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       _snake_case     		=				7
       _snake_case     		=				[F'''<madeupword{i}>''' for i in range(self.num_madeup_words	)]

       _snake_case     		=				kwargs.get("additional_special_tokens"						,   []	)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

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

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(lowerCamelCase	)	)
       _snake_case     		=				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'

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       _snake_case     		=				1

       # Mimic fairseq token-to-id alignment for the first 4 token
       _snake_case     		=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

       _snake_case     		=				len(self.sp_model	)
       _snake_case     		=				{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
       self.fairseq_tokens_to_ids.update(lowerCamelCase	)

       _snake_case     		=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(					self	):
       _snake_case     		=				self.__dict__.copy()
       _snake_case     		=				None
       _snake_case     		=				self.sp_model.serialized_model_proto()
       return state

     def __setstate__(					self						,   lowerCamelCase	):
       _snake_case     		=				d

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

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.sep_token_id]

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

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				{self.convert_ids_to_tokens(lowerCamelCase	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.sp_model.encode(lowerCamelCase						,   out_type=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       _snake_case     		=				self.sp_model.PieceToId(lowerCamelCase	)

       # 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 	UpperCamelCase(					self						,   lowerCamelCase	):
       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 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"".join(lowerCamelCase	).replace(lowerCamelCase						,   " "	).strip()
       return out_string






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

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

       return (out_vocab_file,)


