


"""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__		:     Optional[Any]    				=				logging.get_logger(__name__)
snake_case__		:     Any    				=				{'''vocab_file''': '''spiece.model'''}

snake_case__		:     Union[str, Any]    				=				{
    '''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__		:     Union[str, Any]    				=				{
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']






     def __init__(	self  :					List[Any]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Optional[int]=False					,				UpperCamelCase_  :					Tuple=False					,				UpperCamelCase_  :					List[str]=False					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					Dict=None					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					List[str]=None					,				UpperCamelCase_  :					Optional[Dict[str, Any]] = None					,				**UpperCamelCase_  :					str					,				):
           lowerCAmelCase					:	Union[str, Any]       =   {} if sp_model_kwargs is None else sp_model_kwargs

           lowerCAmelCase					:	Any       =   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'''				)
                 lowerCAmelCase					:	Optional[Any]       =   '''None'''

           # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
           lowerCAmelCase					:	Dict       =   '''<|endoftext|>''' if eos_token is None else eos_token
           lowerCAmelCase					:	int       =   '''<unk>''' if unk_token is None else unk_token
           if "gpt-sw3-7b" in name_or_path:
                 lowerCAmelCase					:	Optional[Any]       =   unk_token if pad_token is None else pad_token
                 lowerCAmelCase					:	Union[str, Any]       =   eos_token if bos_token is None else bos_token
           else:
                 lowerCAmelCase					:	int       =   '''<pad>''' if pad_token is None else pad_token
                 lowerCAmelCase					:	Tuple       =   '''<s>''' if bos_token is None else bos_token

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

           lowerCAmelCase					:	str       =   do_lower_case
           lowerCAmelCase					:	Union[str, Any]       =   remove_space
           lowerCAmelCase					:	Dict       =   keep_accents
           lowerCAmelCase					:	Optional[int]       =   vocab_file

           lowerCAmelCase					:	Optional[Any]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(UpperCamelCase_				)

           # Used for whitespace normalization in input texts
           # fmt : off
           lowerCAmelCase					:	int       =   {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
           # fmt : on

           # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
           lowerCAmelCase					:	List[Any]       =   re.compile(
               F'''[{"".join(map(UpperCamelCase_					,				list(range(0					,				9				)				) + list(range(1_1					,				3_2				)				) + list(range(1_2_7					,				1_6_0				)				) + [1_6_0, 1_7_3, 8_2_0_3]				)				)}]'''				)






     def __getstate__(	self  :					Dict				):
           lowerCAmelCase					:	Tuple       =   self.__dict__.copy()
           lowerCAmelCase					:	Optional[Any]       =   None
           return state






     def __setstate__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   d

           # for backward compatibility
           if not hasattr(self					,				'''sp_model_kwargs'''				):
                 lowerCAmelCase					:	Union[str, Any]       =   {}

           lowerCAmelCase					:	int       =   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  :					Any				):
           return len(self.sp_model				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Any       =   self.non_printing_characters_re.sub(''''''					,				UpperCamelCase_				)

           # Normalize whitespaces
           lowerCAmelCase					:	str       =   ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]				)

           # NFC Unicode normalization
           lowerCAmelCase					:	Optional[Any]       =   unicodedata.normalize('''NFC'''					,				UpperCamelCase_				)
           return text






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str					,				**UpperCamelCase_  :					int				):
           lowerCAmelCase					:	int       =   self.preprocess_text(UpperCamelCase_				)
           return self.sp_model.encode(UpperCamelCase_					,				out_type=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           return self.sp_model.PieceToId(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					int				):
           return self.sp_model.IdToPiece(UpperCamelCase_				)






     @staticmethod
     def      lowerCamelCase__     (	UpperCamelCase_  :					str				):
           return out_string






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Tuple       =   []
           lowerCAmelCase					:	Optional[int]       =   ''''''
           lowerCAmelCase					:	Union[str, Any]       =   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(UpperCamelCase_				) + token
                       lowerCAmelCase					:	int       =   True
                       lowerCAmelCase					:	Dict       =   []
                 else:
                       current_sub_tokens.append(UpperCamelCase_				)
                       lowerCAmelCase					:	Optional[Any]       =   False
           out_string += self.sp_model.decode(UpperCamelCase_				)

           return out_string






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Dict       =   {self.convert_ids_to_tokens(UpperCamelCase_				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	int       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

           return (out_vocab_file,)






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

           if isinstance(UpperCamelCase_					,				UpperCamelCase_				):
                 lowerCAmelCase					:	Any       =   self.preprocess_text(UpperCamelCase_				)
                 lowerCAmelCase					:	Optional[int]       =   self.sp_model.encode(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Any       =   [self.preprocess_text(UpperCamelCase_				) for t in text]
                 lowerCAmelCase					:	Union[str, Any]       =   self.sp_model.encode(UpperCamelCase_				)

           if return_tensors is True or return_tensors == "pt":
                 lowerCAmelCase					:	List[Any]       =   torch.tensor(UpperCamelCase_				)

           return token_ids






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Union[int, List[int]]				):
           return self.sp_model.decode(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					"Conversation"				):
           lowerCAmelCase					:	Dict       =   [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
           lowerCAmelCase					:	List[str]       =   (
               F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(UpperCamelCase_				) + F'''{self.bos_token}Bot:'''
           )
           return self.encode(text=UpperCamelCase_				)






"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 snake_case__		:     int    				=				'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			Any ,   _snake_case				:			str=None ,   _snake_case				:			str=None ,   _snake_case				:			Dict=None ,   _snake_case				:			Tuple=None ,   _snake_case				:			str=None ,   _snake_case				:			Any=None ,   ):
      if attention_mask is None:
            lowerCAmelCase					:	List[str]       =   np.where(input_ids != config.pad_token_id ,   1 ,   0			)
      if decoder_attention_mask is None:
            lowerCAmelCase					:	Optional[int]       =   np.where(decoder_input_ids != config.pad_token_id ,   1 ,   0			)
      if head_mask is None:
            lowerCAmelCase					:	Any       =   np.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            lowerCAmelCase					:	int       =   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            lowerCAmelCase					:	List[str]       =   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					int=1_3					,				UpperCamelCase_  :					Union[str, Any]=7					,				UpperCamelCase_  :					Union[str, Any]=True					,				UpperCamelCase_  :					List[Any]=False					,				UpperCamelCase_  :					Dict=9_9					,				UpperCamelCase_  :					Optional[int]=1_6					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					List[str]=4					,				UpperCamelCase_  :					List[Any]=4					,				UpperCamelCase_  :					int="gelu"					,				UpperCamelCase_  :					Optional[int]=0.1					,				UpperCamelCase_  :					Any=0.1					,				UpperCamelCase_  :					str=3_2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					Tuple=1					,				UpperCamelCase_  :					List[Any]=0					,				UpperCamelCase_  :					Any=0.02					,				):
           lowerCAmelCase					:	Tuple       =   parent
           lowerCAmelCase					:	str       =   batch_size
           lowerCAmelCase					:	List[Any]       =   seq_length
           lowerCAmelCase					:	Optional[int]       =   is_training
           lowerCAmelCase					:	int       =   use_labels
           lowerCAmelCase					:	List[Any]       =   vocab_size
           lowerCAmelCase					:	str       =   hidden_size
           lowerCAmelCase					:	List[Any]       =   num_hidden_layers
           lowerCAmelCase					:	Any       =   num_attention_heads
           lowerCAmelCase					:	List[Any]       =   intermediate_size
           lowerCAmelCase					:	Optional[int]       =   hidden_act
           lowerCAmelCase					:	Dict       =   hidden_dropout_prob
           lowerCAmelCase					:	Optional[int]       =   attention_probs_dropout_prob
           lowerCAmelCase					:	List[Any]       =   max_position_embeddings
           lowerCAmelCase					:	Union[str, Any]       =   eos_token_id
           lowerCAmelCase					:	Dict       =   pad_token_id
           lowerCAmelCase					:	Optional[Any]       =   bos_token_id
           lowerCAmelCase					:	List[str]       =   initializer_range






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[Any]       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size				)					,				3					,				self.vocab_size				)
           lowerCAmelCase					:	str       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,				dtype=np.intaa				))					,				-1				)

           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)

           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=self.hidden_size					,				encoder_layers=self.num_hidden_layers					,				decoder_layers=self.num_hidden_layers					,				encoder_attention_heads=self.num_attention_heads					,				decoder_attention_heads=self.num_attention_heads					,				encoder_ffn_dim=self.intermediate_size					,				decoder_ffn_dim=self.intermediate_size					,				dropout=self.hidden_dropout_prob					,				attention_dropout=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				eos_token_id=self.eos_token_id					,				bos_token_id=self.bos_token_id					,				pad_token_id=self.pad_token_id					,				initializer_range=self.initializer_range					,				use_cache=UpperCamelCase_					,				)
           lowerCAmelCase					:	Union[str, Any]       =   prepare_blenderbot_inputs_dict(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self.prepare_config_and_inputs()
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   2_0
           lowerCAmelCase					:	Tuple       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   model.encode(inputs_dict['''input_ids''']				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase					:	str       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,				dtype='''i4'''				)

           lowerCAmelCase					:	Tuple       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)
           lowerCAmelCase					:	List[Any]       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Any       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4'''				)
           lowerCAmelCase					:	List[str]       =   model.decode(
               decoder_input_ids[:, -1:]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Union[str, Any]       =   model.decode(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	int       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
           self.parent.assertTrue(diff < 1E-3					,				msg=F'''Max diff is {diff}'''				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   2_0
           lowerCAmelCase					:	List[Any]       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   model.encode(inputs_dict['''input_ids''']				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase					:	str       =   jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
               ]					,				axis=-1					,				)

           lowerCAmelCase					:	Union[str, Any]       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	str       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)

           lowerCAmelCase					:	Dict       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)
           lowerCAmelCase					:	Any       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4'''				)
           lowerCAmelCase					:	Union[str, Any]       =   model.decode(
               decoder_input_ids[:, -1:]					,				UpperCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_attention_mask=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Dict       =   model.decode(UpperCamelCase_					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
           self.parent.assertTrue(diff < 1E-3					,				msg=F'''Max diff is {diff}'''				)



@require_flax
class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					99






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	List[Any]       =   np.array(
               [
                   [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                   [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                   [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                   [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                   [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                   [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                   [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                   [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                   [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                   [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                   [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                   [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                   [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
               ]					,				dtype=np.intaa					,				)

           lowerCAmelCase					:	List[Any]       =   input_ids.shape[0]
           lowerCAmelCase					:	Optional[Any]       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=2_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=3_2					,				decoder_ffn_dim=3_2					,				max_position_embeddings=4_8					,				eos_token_id=2					,				pad_token_id=1					,				bos_token_id=0					,				)
           return config, input_ids, batch_size






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_config_and_data()
           lowerCAmelCase					:	Any       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   lm_model(input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Any       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=1_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=8					,				decoder_ffn_dim=8					,				max_position_embeddings=4_8					,				)
           lowerCAmelCase					:	int       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[str]       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[Any]       =   lm_model(input_ids=UpperCamelCase_					,				decoder_input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Any       =   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)
           lowerCAmelCase					:	Optional[int]       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           lowerCAmelCase					:	str       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           self.assertEqual(shifted.shape					,				input_ids.shape				)
           self.assertEqual(UpperCamelCase_					,				n_pad_before - 1				)
           self.assertTrue(np.equal(shifted[:, 0]					,				2				).all()				)



@require_flax
class 		snake_case_(       a__							,					unittest.TestCase							,					a__			):
     __UpperCamelCase  =					True
     __UpperCamelCase  =					(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     __UpperCamelCase  =					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Any       =   FlaxBlenderbotModelTester(self				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	str       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Optional[int]       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_				)
                       lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)

                       @jax.jit
                       def encode_jitted(UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Optional[Any]=None					,				**UpperCamelCase_  :					List[str]				):
                             return model.encode(input_ids=UpperCamelCase_					,				attention_mask=UpperCamelCase_				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	List[str]       =   encode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	int       =   encode_jitted(**UpperCamelCase_				).to_tuple()

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Tuple       =   model_class(UpperCamelCase_				)
                       lowerCAmelCase					:	int       =   model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask''']				)

                       lowerCAmelCase					:	List[Any]       =   {
                           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                           '''encoder_outputs''': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					int				):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				encoder_outputs=UpperCamelCase_					,				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	str       =   decode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	Union[str, Any]       =   decode_jitted(**UpperCamelCase_				).to_tuple()

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






     @slow
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase					:	Optional[int]       =   model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 lowerCAmelCase					:	int       =   np.ones((1, 1)				) * model.config.eos_token_id
                 lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     @unittest.skipUnless(jax_device != '''cpu'''					,				'''3B test too slow on CPU.'''				)
     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
           lowerCAmelCase					:	List[str]       =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

           lowerCAmelCase					:	Tuple       =   FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''					,				from_pt=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

           lowerCAmelCase					:	List[Any]       =   ['''Sam''']
           lowerCAmelCase					:	str       =   tokenizer(UpperCamelCase_					,				return_tensors='''jax'''				)

           lowerCAmelCase					:	Union[str, Any]       =   model.generate(**UpperCamelCase_					,				**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   '''Sam is a great name. It means "sun" in Gaelic.'''

           lowerCAmelCase					:	Union[str, Any]       =   tokenizer.batch_decode(UpperCamelCase_					,				**UpperCamelCase_				)
           assert generated_txt[0].strip() == tgt_text


