"""simple docstring"""



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Optional[int]	,      A_							:     Union[str, Any]=13	,      A_							:     Optional[Any]=30	,      A_							:     Dict=2	,      A_							:     Tuple=3	,      A_							:     Tuple=True	,      A_							:     Tuple=True	,      A_							:     str=32	,      A_							:     List[Any]=5	,      A_							:     List[str]=4	,      A_							:     Dict=37	,      A_							:     List[Any]="gelu"	,      A_							:     Union[str, Any]=0.1	,      A_							:     Optional[Any]=0.1	,      A_							:     Optional[int]=10	,      A_							:     List[str]=0.02	,      )    ->       Tuple:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							ViTConfig(
             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=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Union[str, Any]	,      A_							:     Optional[int]    )    ->       str:
         __snake_case				=							FlaxViTModel(config=A_    )
         __snake_case				=							model(A_    )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(self.image_size, self.image_size)
         __snake_case				=							(self.patch_size, self.patch_size)
         __snake_case				=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, num_patches + 1, self.hidden_size)    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     List[str]	,      A_							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxViTForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxViTForImageClassification(A_    )

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







    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case	
         )		,	(
             __snake_case	
         )		,		
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Any							=					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def  lowercase					(		self							:     int    )    ->       None:
         __snake_case				=							FlaxViTModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     str    )    ->       Any:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     str    )    ->       Dict:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

                   @jax.jit
                   def model_jitted(A_							:     str	,      **A_							:     int    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							model_jitted(**A_    ).to_tuple()

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







    @slow
    def  lowercase					(		self							:     int    )    ->       Dict:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )


"""simple docstring"""



import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__lowercase		:		int														=							logging.get_logger(__name__)


__lowercase		:		Tuple														=							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowercase		:		List[Any]														=							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowercase		:		List[str]														=							{"facebook/blenderbot-3B": 128}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					int							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Tuple							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					int							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					str							=					BlenderbotTokenizer
    def __init__(		self							:     Union[str, Any]	,      A_							:     Any=None	,      A_							:     Optional[int]=None	,      A_							:     Optional[Any]=None	,      A_							:     Union[str, Any]="replace"	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     List[Any]="<s>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     Any="<pad>"	,      A_							:     List[str]="<mask>"	,      A_							:     int=False	,      A_							:     Tuple=True	,      **A_							:     Optional[Any]	,      )    ->       int:
         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_	,      )

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

         __snake_case				=							add_prefix_space

         __snake_case				=							'''post_processor'''
         __snake_case				=							getattr(self.backend_tokenizer	,      A_	,      A_    )
         if tokenizer_component_instance:
              __snake_case				=							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:
                   __snake_case				=							tuple(state['''sep''']    )
              if "cls" in state:
                   __snake_case				=							tuple(state['''cls''']    )

              __snake_case				=							False

              if state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
                   __snake_case				=							add_prefix_space
                   __snake_case				=							True

              if state.get('''trim_offsets'''	,      A_    ) != trim_offsets:
                   __snake_case				=							trim_offsets
                   __snake_case				=							True

              if changes_to_apply:
                   __snake_case				=							getattr(A_	,      state.pop('''type'''    )    )
                   __snake_case				=							component_class(**A_    )
                   setattr(self.backend_tokenizer	,      A_	,      A_    )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         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  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else value
         __snake_case				=							value
    def  lowercase					(		self							:     Any	,      *A_							:     List[str]	,      **A_							:     Optional[int]    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )
         assert self.add_prefix_space or not is_split_into_words, (
             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  lowercase					(		self							:     str	,      *A_							:     Tuple	,      **A_							:     str    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )

         assert self.add_prefix_space or not is_split_into_words, (
             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  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							self._tokenizer.model.save(A_	,      name=A_    )
         return tuple(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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]
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       Any:
         return token_ids_a + [self.eos_token_id]







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     "Conversation"    )    ->       List[int]:
         __snake_case				=							[]
         for is_user, text in conversation.iter_texts():
              if is_user:
                   # We need to space prefix as it's being done within blenderbot
                   inputs.append(''' ''' + text    )
              else:
                   # Generated responses should contain them already.
                   inputs.append(A_    )

         __snake_case				=							'''  '''.join(A_    )
         __snake_case				=							self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __snake_case				=							input_ids[-self.model_max_length :]
              logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."    )
         return input_ids