





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)


lowercase    : List[str]    	=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowercase    : str    	=  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowercase    : Any    	=  {
    """RUCAIBox/mvp""": 1024,
}

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	  =       ["input_ids", "attention_mask"]
     __A	  =       MvpTokenizer

     def __init__(      self,       A=None,       A=None,       A=None,       A="replace",       A="<s>",       A="</s>",       A="</s>",       A="<s>",       A="<unk>",       A="<pad>",       A="<mask>",       A=False,       A=True,       **A,       ):





            """simple docstring"""

            super().__init__(
                A,       A,       tokenizer_file=A,       errors=A,       bos_token=A,       eos_token=A,       sep_token=A,       cls_token=A,       unk_token=A,       pad_token=A,       mask_token=A,       add_prefix_space=A,       trim_offsets=A,       **A,       )

            _UpperCamelCase              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
            if pre_tok_state.get('''add_prefix_space''',       A      ) != add_prefix_space:
                   _UpperCamelCase              =						getattr(A,       pre_tok_state.pop('''type'''      )      )
                   _UpperCamelCase              =						add_prefix_space
                   _UpperCamelCase              =						pre_tok_class(**A      )

            _UpperCamelCase              =						add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            _UpperCamelCase              =						'''post_processor'''
            _UpperCamelCase              =						getattr(self.backend_tokenizer,       A,       A      )
            if tokenizer_component_instance:
                   _UpperCamelCase              =						json.loads(tokenizer_component_instance.__getstate__()      )

                   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                   if "sep" in state:
                          _UpperCamelCase              =						tuple(state['''sep''']      )
                   if "cls" in state:
                          _UpperCamelCase              =						tuple(state['''cls''']      )

                   _UpperCamelCase              =						False

                   if state.get('''add_prefix_space''',       A      ) != add_prefix_space:
                          _UpperCamelCase              =						add_prefix_space
                          _UpperCamelCase              =						True

                   if state.get('''trim_offsets''',       A      ) != trim_offsets:
                          _UpperCamelCase              =						trim_offsets
                          _UpperCamelCase              =						True

                   if changes_to_apply:
                          _UpperCamelCase              =						getattr(A,       state.pop('''type'''      )      )
                          _UpperCamelCase              =						component_class(**A      )
                          setattr(self.backend_tokenizer,       A,       A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            if self._mask_token is None:
                   if self.verbose:
                          logger.error('''Using mask_token, but it is not set yet.'''      )
                   return None
            return str(self._mask_token      )

     @mask_token.setter
     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else value
            _UpperCamelCase              =						value

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						kwargs.get('''is_split_into_words''',       A      )

            if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                       '''to use it with pretokenized inputs.'''      )

            return super()._batch_encode_plus(*A,       **A      )

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						kwargs.get('''is_split_into_words''',       A      )

            if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                       '''to use it with pretokenized inputs.'''      )

            return super()._encode_plus(*A,       **A      )

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						self._tokenizer.model.save(A,       name=A      )
            return tuple(A      )

     def 	_UpperCamelCase     (      self,       A,       A=None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                   return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]

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













import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

@slow
@require_flax
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            super().tearDown()
            gc.collect()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                '''stabilityai/stable-diffusion-2''',       revision='''bf16''',       dtype=jnp.bfloataa,       )

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''stabilityai/stable-diffusion-2'''
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxDPMSolverMultistepScheduler.from_pretrained(A,       subfolder='''scheduler'''      )
            _UpperCamelCase       ,		_UpperCamelCase              =						FlaxStableDiffusionPipeline.from_pretrained(
                A,       scheduler=A,       revision='''bf16''',       dtype=jnp.bfloataa,       )
            _UpperCamelCase              =						scheduler_params

            _UpperCamelCase              =						'''A painting of a squirrel eating a burger'''
            _UpperCamelCase              =						jax.device_count()
            _UpperCamelCase              =						num_samples * [prompt]
            _UpperCamelCase              =						sd_pipe.prepare_inputs(A      )

            _UpperCamelCase              =						replicate(A      )
            _UpperCamelCase              =						shard(A      )

            _UpperCamelCase              =						jax.random.PRNGKey(0      )
            _UpperCamelCase              =						jax.random.split(A,       jax.device_count()      )

            _UpperCamelCase              =						sd_pipe(A,       A,       A,       num_inference_steps=25,       jit=A      )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

            _UpperCamelCase              =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
            _UpperCamelCase              =						images[0, 253:256, 253:256, -1]

            _UpperCamelCase              =						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
            _UpperCamelCase              =						jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]      )
            print(F'''output_slice: {output_slice}'''      )
            assert jnp.abs(output_slice - expected_slice      ).max() < 1E-2






