






'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class        _UpperCAmelCase						:
    __lowerCamelCase:   List[Any]		       =    BlenderbotConfig
    __lowerCamelCase:   Dict		       =    {}
    __lowerCamelCase:   int		       =    'gelu'




    def __init__(			self  :       Optional[Any]  ,     a  :       Union[str, Any]  ,     a  :       List[Any]=1_3  ,     a  :       Dict=7  ,     a  :       Dict=True  ,     a  :       Optional[int]=False  ,     a  :       int=9_9  ,     a  :       List[str]=3_2  ,     a  :       int=2  ,     a  :       Tuple=4  ,     a  :       Optional[Any]=3_7  ,     a  :       str=0.1  ,     a  :       Dict=0.1  ,     a  :       Optional[int]=2_0  ,     a  :       str=2  ,     a  :       int=1  ,     a  :       int=0  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     parent
       lowercase_	:    Optional[int]           =     batch_size
       lowercase_	:    int           =     seq_length
       lowercase_	:    Dict           =     is_training
       lowercase_	:    List[str]           =     use_labels
       lowercase_	:    Union[str, Any]           =     vocab_size
       lowercase_	:    int           =     hidden_size
       lowercase_	:    Optional[int]           =     num_hidden_layers
       lowercase_	:    str           =     num_attention_heads
       lowercase_	:    List[Any]           =     intermediate_size

       lowercase_	:    Optional[int]           =     hidden_dropout_prob
       lowercase_	:    List[Any]           =     attention_probs_dropout_prob
       lowercase_	:    Any           =     max_position_embeddings
       lowercase_	:    Dict           =     eos_token_id
       lowercase_	:    List[Any]           =     pad_token_id
       lowercase_	:    Optional[Any]           =     bos_token_id




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






       '''simple docstring'''






       lowercase_	:    Any           =     ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size			)
       lowercase_	:    int           =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)  ,     1			)
       lowercase_	:    int           =     tf.concat([input_ids, eos_tensor]  ,     axis=1			)

       lowercase_	:    int           =     ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size			)

       lowercase_	:    Tuple           =     self.config_cls(
           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_ids=[2]  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     decoder_start_token_id=self.pad_token_id  ,     **self.config_updates  ,     )
       lowercase_	:    Union[str, Any]           =     prepare_blenderbot_inputs_dict(a  ,     a  ,     a			)
       return config, inputs_dict





    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Any  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     TFBlenderbotModel(config=a			).get_decoder()
       lowercase_	:    List[str]           =     inputs_dict["input_ids"]

       lowercase_	:    List[Any]           =     input_ids[:1, :]
       lowercase_	:    int           =     inputs_dict["attention_mask"][:1, :]
       lowercase_	:    Any           =     inputs_dict["head_mask"]
       lowercase_	:    List[Any]           =     1

       # first forward pass
       lowercase_	:    Optional[Any]           =     model(a  ,     attention_mask=a  ,     head_mask=a  ,     use_cache=a			)

       lowercase_    ,		lowercase_	:    int           =     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowercase_	:    str           =     ids_tensor((self.batch_size, 3)  ,     config.vocab_size			)
       lowercase_	:    List[str]           =     tf.cast(ids_tensor((self.batch_size, 3)  ,     2			)  ,     tf.inta			)

       # append to next input_ids and
       lowercase_	:    List[Any]           =     tf.concat([input_ids, next_tokens]  ,     axis=-1			)
       lowercase_	:    str           =     tf.concat([attention_mask, next_attn_mask]  ,     axis=-1			)

       lowercase_	:    List[Any]           =     model(a  ,     attention_mask=a			)[0]
       lowercase_	:    Union[str, Any]           =     model(a  ,     attention_mask=a  ,     past_key_values=a			)[0]

       self.parent.assertEqual(next_tokens.shape[1]  ,     output_from_past.shape[1]			)

       # select random slice
       lowercase_	:    List[Any]           =     int(ids_tensor((1,)  ,     output_from_past.shape[-1]			)			)
       lowercase_	:    Union[str, Any]           =     output_from_no_past[:, -3:, random_slice_idx]
       lowercase_	:    Optional[int]           =     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(a  ,     a  ,     rtol=1e-3			)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		):


   """simple docstring"""




   if attention_mask is None:
      lowercase_	:    Dict           =     tf.cast(tf.math.not_equal(_UpperCamelCase						,		config.pad_token_id							)						,		tf.inta							)
   if decoder_attention_mask is None:
      lowercase_	:    List[Any]           =     tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape						,		dtype=tf.inta							),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,		config.pad_token_id							)						,		tf.inta							),
          ]						,		axis=-1						,		)
   if head_mask is None:
      lowercase_	:    Any           =     tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
   if decoder_head_mask is None:
      lowercase_	:    Dict           =     tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
   if cross_attn_head_mask is None:
      lowercase_	:    Dict           =     tf.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": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class        _UpperCAmelCase						(		snake_case				,					snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Union[str, Any]		       =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    __lowerCamelCase:   Optional[Any]		       =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase:   Any		       =    (
        {
            'conversational': TFBlenderbotForConditionalGeneration,
            'feature-extraction': TFBlenderbotModel,
            'summarization': TFBlenderbotForConditionalGeneration,
            'text2text-generation': TFBlenderbotForConditionalGeneration,
            'translation': TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase:   Dict		       =    True
    __lowerCamelCase:   List[str]		       =    False
    __lowerCamelCase:   int		       =    False




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     TFBlenderbotModelTester(self			)
       lowercase_	:    Union[str, Any]           =     ConfigTester(self  ,     config_class=a			)




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       self.config_tester.run_common_tests()





    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a			)


@require_tokenizers
@require_tf
class        _UpperCAmelCase						(		unittest.TestCase			):
    __lowerCamelCase:   Any		       =    ['My friends are cool but they eat too many carbs.']
    __lowerCamelCase:   Tuple		       =    'facebook/blenderbot-400M-distill'




    @cached_property
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       return BlenderbotTokenizer.from_pretrained(self.model_name			)




    @cached_property
    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
       return model





    @slow
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     self.tokenizer(self.src_text  ,     return_tensors="tf"			)
       lowercase_	:    Dict           =     self.model.generate(
           model_inputs.input_ids  ,     )
       lowercase_	:    List[str]           =     self.tokenizer.batch_decode(generated_ids.numpy()  ,     skip_special_tokens=a			)[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )












'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   List[Any]		       =    'gpt_neox_japanese'




    def __init__(			self  :       List[str]  ,     a  :       List[Any]=3_2_0_0_0  ,     a  :       Union[str, Any]=2_5_6_0  ,     a  :       Optional[Any]=3_2  ,     a  :       Any=3_2  ,     a  :       str=4  ,     a  :       Optional[int]="gelu"  ,     a  :       Optional[Any]=1.00  ,     a  :       Dict=1_0_0_0_0  ,     a  :       List[Any]=2_0_4_8  ,     a  :       Dict=0.02  ,     a  :       int=1e-5  ,     a  :       Optional[int]=True  ,     a  :       Union[str, Any]=3_1_9_9_6  ,     a  :       List[Any]=3_1_9_9_9  ,     a  :       List[str]=0.1  ,     a  :       Dict=0.0  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       super().__init__(bos_token_id=a  ,     eos_token_id=a  ,     **a			)
       lowercase_	:    int           =     vocab_size
       lowercase_	:    int           =     max_position_embeddings
       lowercase_	:    List[str]           =     hidden_size
       lowercase_	:    Any           =     num_hidden_layers
       lowercase_	:    Any           =     num_attention_heads
       lowercase_	:    List[Any]           =     intermediate_multiple_size
       lowercase_	:    List[str]           =     hidden_act
       lowercase_	:    Optional[int]           =     rotary_pct
       lowercase_	:    Tuple           =     rotary_emb_base
       lowercase_	:    Optional[Any]           =     initializer_range
       lowercase_	:    List[str]           =     layer_norm_eps
       lowercase_	:    List[str]           =     use_cache
       lowercase_	:    Any           =     attention_dropout
       lowercase_	:    List[Any]           =     hidden_dropout




