




'''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     _a						:
       __a       :  int            =      BlenderbotSmallConfig
       __a       :  Optional[Any]            =      {}
       __a       :  Any            =      """gelu"""
       def __init__(			self		:			Any     ,       lowercase		:			Any     ,       lowercase		:			Dict=13     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Any=False     ,       lowercase		:			Optional[Any]=99     ,       lowercase		:			List[Any]=32     ,       lowercase		:			List[Any]=2     ,       lowercase		:			Optional[Any]=4     ,       lowercase		:			Optional[Any]=37     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Optional[int]=0.1     ,       lowercase		:			Optional[int]=20     ,       lowercase		:			Tuple=2     ,       lowercase		:			List[Any]=1     ,       lowercase		:			Optional[Any]=0     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size

           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	eos_token_id
           UpperCAmelCase     =	pad_token_id
           UpperCAmelCase     =	bos_token_id
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

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

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

           UpperCAmelCase     =	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     ,       )
           UpperCAmelCase     =	prepare_blenderbot_small_inputs_dict(lowercase     ,       lowercase     ,       lowercase       )
           return config, inputs_dict







       def 					A   (			self		:			str     ,       lowercase		:			List[str]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFBlenderbotSmallModel(config=lowercase       ).get_decoder()
           UpperCAmelCase     =	inputs_dict['''input_ids''']

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

           # first forward pass
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       head_mask=lowercase     ,       use_cache=lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	outputs.to_tuple()

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

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

           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase       )[0]
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       past_key_values=lowercase       )[0]

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

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

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






def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Dict					,     _a			:							Any					,     _a			:							int=None					,     _a			:							Dict=None					,     _a			:							List[str]=None					,     _a			:							Tuple=None					,     _a			:							int=None					,     ):
    if attention_mask is None:
        UpperCAmelCase     =	tf.cast(tf.math.not_equal(_a					,     config.pad_token_id  )					,     tf.inta  )
    if decoder_attention_mask is None:
        UpperCAmelCase     =	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:
        UpperCAmelCase     =	tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
    if decoder_head_mask is None:
        UpperCAmelCase     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
    if cross_attn_head_mask is None:
        UpperCAmelCase     =	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     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Optional[int]            =      (
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       __a       :  Optional[int]            =      (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       __a       :  Optional[Any]            =      (
           {
               """conversational""": TFBlenderbotSmallForConditionalGeneration,
               """feature-extraction""": TFBlenderbotSmallModel,
               """summarization""": TFBlenderbotSmallForConditionalGeneration,
               """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
               """translation""": TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __a       :  int            =      True
       __a       :  Any            =      False
       __a       :  List[Any]            =      False
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFBlenderbotSmallModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()







       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*lowercase       )






@require_tokenizers
@require_tf
class     _a						(			unittest.TestCase				):
       __a       :  str            =      [
           """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?"""
       ]
       __a       :  Any            =      """facebook/blenderbot_small-90M"""
       @cached_property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
       @cached_property
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
           return model







       @slow
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.tokenizer(self.src_text     ,       return_tensors='''tf'''       )
           UpperCAmelCase     =	self.model.generate(
               model_inputs.input_ids     ,       attention_mask=model_inputs.attention_mask     ,       num_beams=2     ,       use_cache=lowercase     ,       )
           UpperCAmelCase     =	self.tokenizer.batch_decode(generated_ids.numpy()     ,       skip_special_tokens=lowercase       )[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 typing import Any


class     _a						:
       def __init__(			self		:			int     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	data
           UpperCAmelCase     =	None






class     _a						:
       def __init__(			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	None
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.head
           while temp is not None:
               print(temp.data     ,       end=''' '''       )
               UpperCAmelCase     =	temp.next
           print()
       def 					A   (			self		:			List[str]     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	Node(lowercase       )
           UpperCAmelCase     =	self.head
           UpperCAmelCase     =	new_node







       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			str       ):



           '''simple docstring'''

           if node_data_a == node_data_a:
               return
           else:
               UpperCAmelCase     =	self.head
               while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase     =	node_a.next

               UpperCAmelCase     =	self.head
               while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase     =	node_a.next

               if node_a is None or node_a is None:
                   return

               UpperCAmelCase ,  UpperCAmelCase     =	node_a.data, node_a.data


if __name__ == "__main__":
   A									=LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('After swapping')
   ll.print_list()




