


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 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							int	   =       BlenderbotConfig
     __lowercase				:							Any	   =       {}
     __lowercase				:							Optional[Any]	   =       'gelu'

     def __init__(					self:Tuple     ,		_a:Optional[Any]     ,		_a:Optional[Any]=13     ,		_a:Tuple=7     ,		_a:Union[str, Any]=True     ,		_a:int=False     ,		_a:int=99     ,		_a:Optional[int]=32     ,		_a:List[str]=2     ,		_a:List[str]=4     ,		_a:List[Any]=37     ,		_a:Any=0.1     ,		_a:int=0.1     ,		_a:List[Any]=20     ,		_a:List[str]=2     ,		_a:int=1     ,		_a:Dict=0     ,		):
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= seq_length
       snake_case__						= is_training
       snake_case__						= use_labels
       snake_case__						= vocab_size
       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size

       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= eos_token_id
       snake_case__						= pad_token_id
       snake_case__						= bos_token_id

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

       snake_case__						= ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size   )

       snake_case__						= 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     ,		)
       snake_case__						= prepare_blenderbot_inputs_dict(_a     ,		_a     ,		_a   )
       return config, inputs_dict







     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Optional[Any]     ,		_a:int   ):
       snake_case__						= TFBlenderbotModel(config=_a   ).get_decoder()
       snake_case__						= inputs_dict['''input_ids''']

       snake_case__						= input_ids[:1, :]
       snake_case__						= inputs_dict['''attention_mask'''][:1, :]
       snake_case__						= inputs_dict['''head_mask''']
       snake_case__						= 1

       # first forward pass
       snake_case__						= model(_a     ,		attention_mask=_a     ,		head_mask=_a     ,		use_cache=_a   )

       snake_case__				,				snake_case__						= outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case__						= ids_tensor((self.batch_size, 3)     ,		config.vocab_size   )
       snake_case__						= tf.cast(ids_tensor((self.batch_size, 3)     ,		2   )     ,		tf.inta   )

       # append to next input_ids and
       snake_case__						= tf.concat([input_ids, next_tokens]     ,		axis=-1   )
       snake_case__						= tf.concat([attention_mask, next_attn_mask]     ,		axis=-1   )

       snake_case__						= model(_a     ,		attention_mask=_a   )[0]
       snake_case__						= 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
       snake_case__						= int(ids_tensor((1,)     ,		output_from_past.shape[-1]   )   )
       snake_case__						= output_from_no_past[:, -3:, random_slice_idx]
       snake_case__						= 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	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   )    ->				Tuple:
  if attention_mask is None:
    snake_case__						= tf.cast(tf.math.not_equal(__lowerCAmelCase							,   config.pad_token_id				)							,   tf.inta				)
  if decoder_attention_mask is None:
    snake_case__						= 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:
    snake_case__						= tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    snake_case__						= tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    snake_case__						= 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 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowercase				:							Any	   =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowercase				:							Tuple	   =       (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowercase				:							Any	   =       True
     __lowercase				:							int	   =       False
     __lowercase				:							int	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFBlenderbotModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.config_tester.run_common_tests()







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_a   )




@require_tokenizers
@require_tf
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ['My friends are cool but they eat too many carbs.']
     __lowercase				:							Optional[int]	   =       'facebook/blenderbot-400M-distill'

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return BlenderbotTokenizer.from_pretrained(self.model_name   )

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
       return model







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.tokenizer(self.src_text     ,		return_tensors='''tf'''   )
       snake_case__						= self.model.generate(
           model_inputs.input_ids     ,		)
       snake_case__						= 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?"
       )





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__							:						List[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'data2vec-vision'

     def __init__(					self:int     ,		_a:Tuple=7_68     ,		_a:int=12     ,		_a:Any=12     ,		_a:Optional[int]=30_72     ,		_a:Optional[int]="gelu"     ,		_a:Any=0.0     ,		_a:Any=0.0     ,		_a:List[str]=0.02     ,		_a:Dict=1e-12     ,		_a:Tuple=2_24     ,		_a:Any=16     ,		_a:str=3     ,		_a:str=False     ,		_a:Union[str, Any]=False     ,		_a:Optional[int]=False     ,		_a:Any=False     ,		_a:Dict=0.1     ,		_a:Dict=0.1     ,		_a:str=True     ,		_a:str=[3, 5, 7, 11]     ,		_a:List[str]=[1, 2, 3, 6]     ,		_a:List[str]=True     ,		_a:Any=0.4     ,		_a:str=2_56     ,		_a:Union[str, Any]=1     ,		_a:int=False     ,		_a:Optional[int]=2_55     ,		**_a:Dict     ,		):
       super().__init__(**_a   )

       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__						= initializer_range
       snake_case__						= layer_norm_eps

       snake_case__						= image_size
       snake_case__						= patch_size
       snake_case__						= num_channels
       snake_case__						= use_mask_token
       snake_case__						= use_absolute_position_embeddings
       snake_case__						= use_relative_position_bias
       snake_case__						= use_shared_relative_position_bias
       snake_case__						= layer_scale_init_value
       snake_case__						= drop_path_rate
       snake_case__						= use_mean_pooling
       # decode head attributes (semantic segmentation)
       snake_case__						= out_indices
       snake_case__						= pool_scales
       # auxiliary head attributes (semantic segmentation)
       snake_case__						= use_auxiliary_head
       snake_case__						= auxiliary_loss_weight
       snake_case__						= auxiliary_channels
       snake_case__						= auxiliary_num_convs
       snake_case__						= auxiliary_concat_input
       snake_case__						= semantic_loss_ignore_index




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       version.parse('1.11'				)

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return 1e-4

