






"""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			:
 UpperCamelCase					:       Union[str, Any]			=					BlenderbotSmallConfig
 UpperCamelCase					:       Tuple			=					{}
 UpperCamelCase					:       str			=					'gelu'


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

   _a  :       Optional[int]             =	hidden_dropout_prob
   _a  :       List[str]             =	attention_probs_dropout_prob
   _a  :       List[Any]             =	max_position_embeddings
   _a  :       int             =	eos_token_id
   _a  :       Tuple             =	pad_token_id
   _a  :       Union[str, Any]             =	bos_token_id


 def _lowercase	(     self		:						Any							)					->       Optional[Any]:
   _a  :       Dict             =	ids_tensor([self.batch_size, self.seq_length - 1]							,						self.vocab_size							)
   _a  :       int             =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)							,						1							)
   _a  :       Tuple             =	tf.concat([input_ids, eos_tensor]							,						axis=1							)

   _a  :       Union[str, Any]             =	ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size							)

   _a  :       Any             =	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							,						)
   _a  :       Union[str, Any]             =	prepare_blenderbot_small_inputs_dict(__UpperCAmelCase							,						__UpperCAmelCase							,						__UpperCAmelCase							)
   return config, inputs_dict







 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Tuple							)					->       List[str]:
   _a  :       int             =	TFBlenderbotSmallModel(config=__UpperCAmelCase							).get_decoder()
   _a  :       int             =	inputs_dict["""input_ids"""]

   _a  :       Union[str, Any]             =	input_ids[:1, :]
   _a  :       Dict             =	inputs_dict["""attention_mask"""][:1, :]
   _a  :       Optional[int]             =	inputs_dict["""head_mask"""]
   _a  :       List[str]             =	1

   # first forward pass
   _a  :       Optional[int]             =	model(__UpperCAmelCase							,						attention_mask=__UpperCAmelCase							,						head_mask=__UpperCAmelCase							,						use_cache=__UpperCAmelCase							)

   _a      ,  _a  :       str             =	outputs.to_tuple()

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

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

   _a  :       List[Any]             =	model(__UpperCAmelCase							,						attention_mask=__UpperCAmelCase							)[0]
   _a  :       Optional[Any]             =	model(__UpperCAmelCase							,						attention_mask=__UpperCAmelCase							,						past_key_values=__UpperCAmelCase							)[0]

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

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

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







def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   ):



  '''simple docstring'''


  if attention_mask is None:
    _a  :       Optional[Any]             =	tf.cast(tf.math.not_equal(__A     ,   config.pad_token_id				)     ,   tf.inta				)
  if decoder_attention_mask is None:
    _a  :       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:
    _a  :       Union[str, Any]             =	tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    _a  :       Optional[int]             =	tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    _a  :       Any             =	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			(	UpperCAmelCase_		,     UpperCAmelCase_		,     unittest.TestCase      ):
 UpperCamelCase					:       List[Any]			=					(
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 UpperCamelCase					:       Union[str, Any]			=					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 UpperCamelCase					:       int			=					(
     {
         'conversational': TFBlenderbotSmallForConditionalGeneration,
         'feature-extraction': TFBlenderbotSmallModel,
         'summarization': TFBlenderbotSmallForConditionalGeneration,
         'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
         'translation': TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase					:       Union[str, Any]			=					True
 UpperCamelCase					:       str			=					False
 UpperCamelCase					:       Optional[Any]			=					False


 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	TFBlenderbotSmallModelTester(self							)
   _a  :       List[str]             =	ConfigTester(self							,						config_class=__UpperCAmelCase							)


 def _lowercase	(     self		:						Tuple							)					->       Tuple:
   self.config_tester.run_common_tests()







 def _lowercase	(     self		:						Tuple							)					->       Dict:
   _a  :       int             =	self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase							)







@require_tokenizers
@require_tf
class        UpperCamelCase			(	unittest.TestCase      ):
 UpperCamelCase					:       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?'
 ]
 UpperCamelCase					:       Dict			=					'facebook/blenderbot_small-90M'


 @cached_property
 def _lowercase	(     self		:						Union[str, Any]							)					->       Any:
   return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""							)


 @cached_property
 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Any             =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
   return model







 @slow
 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Dict             =	self.tokenizer(self.src_text							,						return_tensors="""tf"""							)
   _a  :       Tuple             =	self.model.generate(
       model_inputs.input_ids							,						attention_mask=model_inputs.attention_mask							,						num_beams=2							,						use_cache=__UpperCAmelCase							,						)
   _a  :       Any             =	self.tokenizer.batch_decode(generated_ids.numpy()							,						skip_special_tokens=__UpperCAmelCase							)[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.",
   )


from __future__ import annotations

from collections import deque





class      A							:


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						list[str]		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  []
         self.adlist.append(
             {"value": "", "next_states": [], "fail_state": 0, "output": []}		)

         for keyword in keywords:
               self.add_keyword(__UpperCAmelCase		)
         self.set_fail_transitions()


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						str		)						->				int | None:
         """simple docstring"""



         for state in self.adlist[current_state]["next_states"]:
               if char == self.adlist[state]["value"]:
                     return state
         return None


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  0
         for character in keyword:
               UpperCAmelCase__								=  self.find_next_state(__UpperCAmelCase       ,  __UpperCAmelCase		)
               if next_state is None:
                     self.adlist.append(
                         {
                             "value": character,
                             "next_states": [],
                             "fail_state": 0,
                             "output": [],
                         }		)
                     self.adlist[current_state]["next_states"].append(len(self.adlist		) - 1		)
                     UpperCAmelCase__								=  len(self.adlist		) - 1
               else:
                     UpperCAmelCase__								=  next_state
         self.adlist[current_state]["output"].append(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[int]		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  deque()
         for node in self.adlist[0]["next_states"]:
               q.append(__UpperCAmelCase		)
               UpperCAmelCase__								=  0
         while q:
               UpperCAmelCase__								=  q.popleft()
               for child in self.adlist[r]["next_states"]:
                     q.append(__UpperCAmelCase		)
                     UpperCAmelCase__								=  self.adlist[r]["fail_state"]
                     while (
                         self.find_next_state(__UpperCAmelCase       ,  self.adlist[child]["value"]		) is None
                         and state != 0
                     ):
                           UpperCAmelCase__								=  self.adlist[state]["fail_state"]
                     UpperCAmelCase__								=  self.find_next_state(
                         __UpperCAmelCase       ,  self.adlist[child]["value"]		)
                     if self.adlist[child]["fail_state"] is None:
                           UpperCAmelCase__								=  0
                     UpperCAmelCase__								=  (
                         self.adlist[child]["output"]
                         + self.adlist[self.adlist[child]["fail_state"]]["output"]
                     )





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				dict[str, list[int]]:
         """simple docstring"""



         UpperCAmelCase__								=  {}  # returns a dict with keywords and list of its occurrences
         UpperCAmelCase__								=  0
         for i in range(len(__UpperCAmelCase		)		):
               while (
                   self.find_next_state(__UpperCAmelCase       ,  string[i]		) is None
                   and current_state != 0
               ):
                     UpperCAmelCase__								=  self.adlist[current_state]["fail_state"]
               UpperCAmelCase__								=  self.find_next_state(__UpperCAmelCase       ,  string[i]		)
               if next_state is None:
                     UpperCAmelCase__								=  0
               else:
                     UpperCAmelCase__								=  next_state
                     for key in self.adlist[current_state]["output"]:
                           if key not in result:
                                 UpperCAmelCase__								=  []
                           result[key].append(i - len(__UpperCAmelCase		) + 1		)
         return result


if __name__ == "__main__":
      import doctest

      doctest.testmod()



