





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 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   Optional[int]      =							BlenderbotSmallConfig
							snake_case  :   Union[str, Any]      =							{}
							snake_case  :   str      =							"""gelu"""






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=13	,			__lowerCAmelCase=7	,			__lowerCAmelCase=True	,			__lowerCAmelCase=False	,			__lowerCAmelCase=99	,			__lowerCAmelCase=32	,			__lowerCAmelCase=2	,			__lowerCAmelCase=4	,			__lowerCAmelCase=37	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=20	,			__lowerCAmelCase=2	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			):
														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 					_lowerCamelCase		(    self	):
														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(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	)
														return config, inputs_dict







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      TFBlenderbotSmallModel(config=_lowerCamelCase	).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(_lowerCamelCase	,			attention_mask=_lowerCamelCase	,			head_mask=_lowerCamelCase	,			use_cache=_lowerCamelCase	)

														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(_lowerCamelCase	,			attention_mask=_lowerCamelCase	)[0]
														UpperCamelCase__					  =      model(_lowerCamelCase	,			attention_mask=_lowerCamelCase	,			past_key_values=_lowerCamelCase	)[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(_lowerCamelCase	,			_lowerCamelCase	,			rtol=1E-3	)






def      _UpperCamelCase  (a__ :str ,      a__ :List[str] ,      a__ :Dict ,      a__ :Optional[Any]=None ,      a__ :Tuple=None ,      a__ :Optional[Any]=None ,      a__ :Union[str, Any]=None ,      a__ :Union[str, Any]=None ,      ):






							"""simple docstring"""





							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 							__SCREAMING_SNAKE_CASE       (  lowerCAmelCase__ ,		lowerCAmelCase__ ,		unittest.TestCase							):
							snake_case  :   Union[str, Any]      =							(
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							snake_case  :   List[Any]      =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							snake_case  :   Any      =							(
							    {
							        """conversational""": TFBlenderbotSmallForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotSmallModel,
							        """summarization""": TFBlenderbotSmallForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
							        """translation""": TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							snake_case  :   str      =							True
							snake_case  :   List[Any]      =							False
							snake_case  :   Tuple      =							False






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      TFBlenderbotSmallModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(self	,			config_class=_lowerCamelCase	)






							def 					_lowerCamelCase		(    self	):
														self.config_tester.run_common_tests()







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase	)






@require_tokenizers
@require_tf
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							snake_case  :   Union[str, Any]      =							[
							    """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?"""
							]
							snake_case  :   Tuple      =							"""facebook/blenderbot_small-90M"""






							@cached_property
							def 					_lowerCamelCase		(    self	):
														return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)






							@cached_property
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
														return model







							@slow
							def 					_lowerCamelCase		(    self	):
														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=_lowerCamelCase	,			)
														UpperCamelCase__					  =      self.tokenizer.batch_decode(generated_ids.numpy()	,			skip_special_tokens=_lowerCamelCase	)[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

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    True
        if right_element < a or left_element > b:
              return -math.inf
        if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
        UpperCAmelCase__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

