


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   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							BlenderbotConfig
       _a   							=							{}
       _a   							=							"gelu"



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

          lowerCamelCase_						:   Any		= hidden_dropout_prob
          lowerCamelCase_						:   Optional[Any]		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= eos_token_id
          lowerCamelCase_						:   List[str]		= pad_token_id
          lowerCamelCase_						:   Any		= bos_token_id



       def 				_lowerCAmelCase							(						self				:				Dict							)					->List[str]:
          lowerCamelCase_						:   List[str]		= ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size							)
          lowerCamelCase_						:   int		= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		,				1							)
          lowerCamelCase_						:   str		= tf.concat([input_ids, eos_tensor]		,				axis=1							)

          lowerCamelCase_						:   List[Any]		= ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

          lowerCamelCase_						:   Optional[int]		= 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		,				)
          lowerCamelCase_						:   Optional[Any]		= prepare_blenderbot_inputs_dict(__a		,				__a		,				__a							)
          return config, inputs_dict




       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				Optional[int]		,				__a				:				Dict							)					->Optional[Any]:
          lowerCamelCase_						:   Dict		= TFBlenderbotModel(config=__a							).get_decoder()
          lowerCamelCase_						:   Any		= inputs_dict["""input_ids"""]

          lowerCamelCase_						:   Tuple		= input_ids[:1, :]
          lowerCamelCase_						:   Union[str, Any]		= inputs_dict["""attention_mask"""][:1, :]
          lowerCamelCase_						:   Any		= inputs_dict["""head_mask"""]
          lowerCamelCase_						:   Optional[int]		= 1

          # first forward pass
          lowerCamelCase_						:   int		= model(__a		,				attention_mask=__a		,				head_mask=__a		,				use_cache=__a							)

          lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= outputs.to_tuple()

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

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

          lowerCamelCase_						:   Optional[int]		= model(__a		,				attention_mask=__a							)[0]
          lowerCamelCase_						:   Dict		= 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
          lowerCamelCase_						:   List[Any]		= int(ids_tensor((1,)		,				output_from_past.shape[-1]							)							)
          lowerCamelCase_						:   Dict		= output_from_no_past[:, -3:, random_slice_idx]
          lowerCamelCase_						:   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    __lowerCamelCase   (      A__						: Any						,						A__						: Optional[int]						,						A__						: List[Any]						,						A__						: Dict=None						,						A__						: Dict=None						,						A__						: Dict=None						,						A__						: str=None						,						A__						: List[str]=None						,						)     ->    Tuple:
   if attention_mask is None:
      lowerCamelCase_						:   Any		= tf.cast(tf.math.not_equal(A__						,						config.pad_token_id					)						,						tf.inta					)
   if decoder_attention_mask is None:
      lowerCamelCase_						:   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:
      lowerCamelCase_						:   str		= tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      lowerCamelCase_						:   List[Any]		= tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      lowerCamelCase_						:   Tuple		= 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   SCREAMING_SNAKE_CASE_			(a__						,       a__						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       _a   							=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       _a   							=							(
           {
               "conversational": TFBlenderbotForConditionalGeneration,
               "feature-extraction": TFBlenderbotModel,
               "summarization": TFBlenderbotForConditionalGeneration,
               "text2text-generation": TFBlenderbotForConditionalGeneration,
               "translation": TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       _a   							=							True
       _a   							=							False
       _a   							=							False



       def 				_lowerCAmelCase							(						self				:				Any							)					->Union[str, Any]:
          lowerCamelCase_						:   Tuple		= TFBlenderbotModelTester(self							)
          lowerCamelCase_						:   Union[str, Any]		= ConfigTester(self		,				config_class=__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->List[str]:
          self.config_tester.run_common_tests()




       def 				_lowerCAmelCase							(						self				:				Any							)					->List[str]:
          lowerCamelCase_						:   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   SCREAMING_SNAKE_CASE_			(unittest.TestCase						):




       '''simple docstring'''



       _a   							=							["My friends are cool but they eat too many carbs."]
       _a   							=							"facebook/blenderbot-400M-distill"



       @cached_property
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Optional[int]:
          return BlenderbotTokenizer.from_pretrained(self.model_name							)



       @cached_property
       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->int:
          lowerCamelCase_						:   Tuple		= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
          return model




       @slow
       def 				_lowerCAmelCase							(						self				:				str							)					->int:
          lowerCamelCase_						:   Optional[Any]		= self.tokenizer(self.src_text		,				return_tensors="""tf"""							)
          lowerCamelCase_						:   Any		= self.model.generate(
              model_inputs.input_ids		,				)
          lowerCamelCase_						:   Dict		= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


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

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

          lowerCamelCase_						:   str		= vocab_size
          lowerCamelCase_						:   List[Any]		= hidden_size
          lowerCamelCase_						:   Optional[int]		= num_hidden_layers
          lowerCamelCase_						:   Optional[Any]		= num_attention_heads
          lowerCamelCase_						:   List[Any]		= hidden_act
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   List[Any]		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= type_vocab_size
          lowerCamelCase_						:   Dict		= initializer_range
          lowerCamelCase_						:   List[Any]		= layer_norm_eps
          lowerCamelCase_						:   List[str]		= projection_dim
          lowerCamelCase_						:   Optional[int]		= position_embedding_type





