


'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     BlenderbotSmallConfig
     __magic_name__           =     {}
     __magic_name__           =     "gelu"







     def __init__(			self   ,						snake_case__   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=2   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=20   ,						snake_case__=2   ,						snake_case__=1   ,						snake_case__=0   ,						):

            '''simple docstring'''

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

            _lowerCAmelCase     :				str       =							hidden_dropout_prob
            _lowerCAmelCase     :				List[str]       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				Tuple       =							max_position_embeddings
            _lowerCAmelCase     :				int       =							eos_token_id
            _lowerCAmelCase     :				Tuple       =							pad_token_id
            _lowerCAmelCase     :				Any       =							bos_token_id







     def a						(			self ):

            '''simple docstring'''

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

            _lowerCAmelCase     :				Tuple       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

            _lowerCAmelCase     :				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     :				Union[str, Any]       =							prepare_blenderbot_small_inputs_dict(snake_case__   ,						snake_case__   ,						snake_case__ )
            return config, inputs_dict







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							TFBlenderbotSmallModel(config=snake_case__ ).get_decoder()
            _lowerCAmelCase     :				str       =							inputs_dict['input_ids']

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

            # first forward pass
            _lowerCAmelCase     :				List[str]       =							model(snake_case__   ,						attention_mask=snake_case__   ,						head_mask=snake_case__   ,						use_cache=snake_case__ )

            _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							outputs.to_tuple()

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

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

            _lowerCAmelCase     :				List[Any]       =							model(snake_case__   ,						attention_mask=snake_case__ )[0]
            _lowerCAmelCase     :				Tuple       =							model(snake_case__   ,						attention_mask=snake_case__   ,						past_key_values=snake_case__ )[0]

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

            # select random slice
            _lowerCAmelCase     :				str       =							int(ids_tensor((1,)   ,						output_from_past.shape[-1] ) )
            _lowerCAmelCase     :				str       =							output_from_no_past[:, -3:, random_slice_idx]
            _lowerCAmelCase     :				Tuple       =							output_from_past[:, :, random_slice_idx]

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






def       lowercase					(_A ,    _A ,    _A ,    _A=None ,    _A=None ,    _A=None ,    _A=None ,    _A=None ,    ):







       """simple docstring"""







       if attention_mask is None:
              _lowerCAmelCase     :				Optional[Any]       =							tf.cast(tf.math.not_equal(_A ,    config.pad_token_id       ) ,    tf.inta       )
       if decoder_attention_mask is None:
              _lowerCAmelCase     :				Dict       =							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     :				int       =							tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              _lowerCAmelCase     :				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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     __magic_name__           =     (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     __magic_name__           =     (
         {
             "conversational": TFBlenderbotSmallForConditionalGeneration,
             "feature-extraction": TFBlenderbotSmallModel,
             "summarization": TFBlenderbotSmallForConditionalGeneration,
             "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
             "translation": TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __magic_name__           =     True
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							TFBlenderbotSmallModelTester(self )
            _lowerCAmelCase     :				Any       =							ConfigTester(self   ,						config_class=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     [
         "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
         "  i'm going to throw up.\nand why is that?"
     ]
     __magic_name__           =     "facebook/blenderbot_small-90M"







     @cached_property
     def a						(			self ):

            '''simple docstring'''

            return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' )







     @cached_property
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
            return model







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.tokenizer(self.src_text   ,						return_tensors='tf' )
            _lowerCAmelCase     :				Tuple       =							self.model.generate(
                model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2   ,						use_cache=snake_case__   ,						)
            _lowerCAmelCase     :				Optional[Any]       =							self.tokenizer.batch_decode(generated_ids.numpy()   ,						skip_special_tokens=snake_case__ )[0]
            assert generated_words in (
                "i don't know. i just feel like i'm going to throw up. it's not fun.",
                "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                "i'm not sure. i just feel like i've been in a bad situation.",
            )





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

