'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
       import jax

       from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor





class       _a    (		unittest.TestCase      ):


  """simple docstring"""

  def __init__(		self      :       Optional[int]   ,				lowercase_      :       int   ,				lowercase_      :       Dict=100   ,				lowercase_      :       Optional[Any]=13   ,				lowercase_      :       List[str]=30   ,				lowercase_      :       Any=2   ,				lowercase_      :       str=3   ,				lowercase_      :       str=True   ,				lowercase_      :       Optional[int]=True   ,				lowercase_      :       Optional[Any]=32   ,				lowercase_      :       Dict=5   ,				lowercase_      :       Optional[Any]=4   ,				lowercase_      :       Optional[int]=37   ,				lowercase_      :       int="gelu"   ,				lowercase_      :       int=0.1   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Any=10   ,				lowercase_      :       Optional[Any]=0.0_2   ,				lowercase_      :       Dict=3   ,				):







    '''simple docstring'''


    lowercase_     =       parent
    lowercase_     =       vocab_size
    lowercase_     =       batch_size
    lowercase_     =       image_size
    lowercase_     =       patch_size
    lowercase_     =       num_channels
    lowercase_     =       is_training
    lowercase_     =       use_labels
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       type_sequence_label_size
    lowercase_     =       initializer_range

    # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    lowercase_     =       (image_size // patch_size) ** 2
    lowercase_     =       num_patches + 1




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

    lowercase_     =       None
    if self.use_labels:
      lowercase_     =       ids_tensor([self.batch_size]   ,				self.type_sequence_label_size      )

    lowercase_     =       BeitConfig(
        vocab_size=self.vocab_size   ,				image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				is_decoder=lowercase_   ,				initializer_range=self.initializer_range   ,				)

    return config, pixel_values, labels




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int   ,				lowercase_      :       Dict   ,				lowercase_      :       int      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitModel(config=lowercase_      )
    lowercase_     =       model(lowercase_      )
    self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       str   ,				lowercase_      :       Tuple   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitForMaskedImageModeling(config=lowercase_      )
    lowercase_     =       model(lowercase_      )
    self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )




  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.type_sequence_label_size
    lowercase_     =       FlaxBeitForImageClassification(config=lowercase_      )
    lowercase_     =       model(lowercase_      )
    self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)      )

    # test greyscale images
    lowercase_     =       1
    lowercase_     =       FlaxBeitForImageClassification(lowercase_      )

    lowercase_     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
    lowercase_     =       model(lowercase_      )





  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.prepare_config_and_inputs()
    (
        (
        lowercase_
    )     ,	(
        lowercase_
    )     ,	(
        lowercase_
    )     ,	
    )     =       config_and_inputs
    lowercase_     =       {"""pixel_values""": pixel_values}
    return config, inputs_dict






@require_flax
class       _a    (		__a ,				unittest.TestCase      ):


  """simple docstring"""

  A_    							=						(
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitModelTester(self      )
    lowercase_     =       ConfigTester(self   ,				config_class=lowercase_   ,				has_text_modality=lowercase_   ,				hidden_size=37      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    self.config_tester.run_common_tests()




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      lowercase_     =       model_class(lowercase_      )
      lowercase_     =       inspect.signature(model.__call__      )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      lowercase_     =       [*signature.parameters.keys()]

      lowercase_     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__      ):
        lowercase_     =       self._prepare_for_class(lowercase_   ,				lowercase_      )
        lowercase_     =       model_class(lowercase_      )

        @jax.jit
        def model_jitted(lowercase_      :       List[Any]   ,				**lowercase_      :       List[str]      ):
          return model(pixel_values=lowercase_   ,				**lowercase_      )

        with self.subTest("""JIT Enabled"""      ):
          lowercase_     =       model_jitted(**lowercase_      ).to_tuple()

        with self.subTest("""JIT Disabled"""      ):
          with jax.disable_jit():
            lowercase_     =       model_jitted(**lowercase_      ).to_tuple()

        self.assertEqual(len(lowercase_      )   ,				len(lowercase_      )      )
        for jitted_output, output in zip(lowercase_   ,				lowercase_      ):
          self.assertEqual(jitted_output.shape   ,				output.shape      )




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowercase_      )





  @slow
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    for model_class_name in self.all_model_classes:
      lowercase_     =       model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""      )
      lowercase_     =       model(np.ones((1, 3, 224, 224)      )      )
      self.assertIsNotNone(lowercase_      )






def   A_      (       )			->int:
  lowercase_     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image





@require_vision
@require_flax
class       _a    (		unittest.TestCase      ):


  """simple docstring"""

  @cached_property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""      ) if is_vision_available() else None




  @slow
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""      )

    lowercase_     =       self.default_image_processor
    lowercase_     =       prepare_img()
    lowercase_     =       image_processor(images=lowercase_   ,				return_tensors="""np"""      ).pixel_values

    # prepare bool_masked_pos
    lowercase_     =       np.ones((1, 196)   ,				dtype=lowercase_      )

    # forward pass
    lowercase_     =       model(pixel_values=lowercase_   ,				bool_masked_pos=lowercase_      )
    lowercase_     =       outputs.logits

    # verify the logits
    lowercase_     =       (1, 196, 8_192)
    self.assertEqual(logits.shape   ,				lowercase_      )

    lowercase_     =       np.array(
        [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]      )

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,				lowercase_   ,				atol=1e-2      )      )




  @slow
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""      )

    lowercase_     =       self.default_image_processor
    lowercase_     =       prepare_img()
    lowercase_     =       image_processor(images=lowercase_   ,				return_tensors="""np"""      )

    # forward pass
    lowercase_     =       model(**lowercase_      )
    lowercase_     =       outputs.logits

    # verify the logits
    lowercase_     =       (1, 1_000)
    self.assertEqual(logits.shape   ,				lowercase_      )

    lowercase_     =       np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]      )

    self.assertTrue(np.allclose(logits[0, :3]   ,				lowercase_   ,				atol=1e-4      )      )

    lowercase_     =       281
    self.assertEqual(logits.argmax(-1      ).item()   ,				lowercase_      )





  @slow
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     =       FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""      )

    lowercase_     =       self.default_image_processor
    lowercase_     =       prepare_img()
    lowercase_     =       image_processor(images=lowercase_   ,				return_tensors="""np"""      )

    # forward pass
    lowercase_     =       model(**lowercase_      )
    lowercase_     =       outputs.logits

    # verify the logits
    lowercase_     =       (1, 21_841)
    self.assertEqual(logits.shape   ,				lowercase_      )

    lowercase_     =       np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]      )

    self.assertTrue(np.allclose(logits[0, :3]   ,				lowercase_   ,				atol=1e-4      )      )

    lowercase_     =       2_396
    self.assertEqual(logits.argmax(-1      ).item()   ,				lowercase_      )


'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__snake_case						  =		logging.get_logger(__name__)
__snake_case						  =		{"""vocab_file""": """spiece.model"""}

__snake_case						  =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

__snake_case						  =		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						VOCAB_FILES_NAMES
  A_    							=						PRETRAINED_VOCAB_FILES_MAP
  A_    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_    							=						['''input_ids''', '''attention_mask''']




  def __init__(		self      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       kwargs.get("""name_or_path"""      )
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""      )
      lowercase_     =       """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    lowercase_     =       """<|endoftext|>""" if eos_token is None else eos_token
    lowercase_     =       """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      lowercase_     =       unk_token if pad_token is None else pad_token
      lowercase_     =       eos_token if bos_token is None else bos_token
    else:
      lowercase_     =       """<pad>""" if pad_token is None else pad_token
      lowercase_     =       """<s>""" if bos_token is None else bos_token

    super().__init__(
        do_lower_case=lowercase_   ,				remove_space=lowercase_   ,				keep_accents=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				pad_token=lowercase_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**lowercase_   ,				)

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

    # Used for whitespace normalization in input texts
    # fmt : off
    lowercase_     =       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.__dict__.copy()
    lowercase_     =       None
    return state




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

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

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




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

    # Normalize whitespaces
    lowercase_     =       """""".join([char if char not in self.whitespaces else """ """ for char in text]      )

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       {self.convert_ids_to_tokens(lowercase_      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


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

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

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


    lowercase_     =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    lowercase_     =       (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowercase_      ) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=lowercase_      )

