





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       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				BlenderbotSmallConfig
	_A													=				{}
	_A													=				"gelu"

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

			lowercase__					:      Optional[Any]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Optional[int]      =		max_position_embeddings
			lowercase__					:      Optional[int]      =		eos_token_id
			lowercase__					:      Optional[Any]      =		pad_token_id
			lowercase__					:      Dict      =		bos_token_id

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

			lowercase__					:      Optional[int]      =		ids_tensor([self.batch_size, self.seq_length],					self.vocab_size			)

			lowercase__					:      Dict      =		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,					)
			lowercase__					:      List[Any]      =		prepare_blenderbot_small_inputs_dict(snake_case__,					snake_case__,					snake_case__			)
			return config, inputs_dict





	def    snake_case__(						self:						Dict,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Any			):
			lowercase__					:      List[str]      =		TFBlenderbotSmallModel(config=snake_case__			).get_decoder()
			lowercase__					:      Dict      =		inputs_dict['input_ids']

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

			# first forward pass
			lowercase__					:      Dict      =		model(snake_case__,					attention_mask=snake_case__,					head_mask=snake_case__,					use_cache=snake_case__			)

			lowercase__   ,				lowercase__					:      Tuple      =		outputs.to_tuple()

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

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

			lowercase__					:      str      =		model(snake_case__,					attention_mask=snake_case__			)[0]
			lowercase__					:      int      =		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
			lowercase__					:      Union[str, Any]      =		int(ids_tensor((1,),					output_from_past.shape[-1]			)			)
			lowercase__					:      str      =		output_from_no_past[:, -3:, random_slice_idx]
			lowercase__					:      Dict      =		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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict   ,    _lowercase     : Optional[int]   ,    _lowercase     : str   ,    _lowercase     : Dict=None   ,    _lowercase     : Optional[int]=None   ,    _lowercase     : Any=None   ,    _lowercase     : List[str]=None   ,    _lowercase     : Optional[Any]=None   ,    )   ->							str:




		'''simple docstring'''


		if attention_mask is None:
				lowercase__					:      Union[str, Any]      =		tf.cast(tf.math.not_equal(_UpperCamelCase   ,    config.pad_token_id			)   ,    tf.inta			)
		if decoder_attention_mask is None:
				lowercase__					:      int      =		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:
				lowercase__					:      Any      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				lowercase__					:      List[str]      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				lowercase__					:      Dict      =		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       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





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

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      Any      =		TFBlenderbotSmallModelTester(self			)
			lowercase__					:      Dict      =		ConfigTester(self,					config_class=snake_case__			)

	def    snake_case__(						self:						Any			):
			self.config_tester.run_common_tests()





	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[Any]      =		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       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	_A													=				[
	    "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													=				"facebook/blenderbot_small-90M"

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

	@cached_property
	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      List[str]      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
			return model





	@slow
	def    snake_case__(						self:						Tuple			):
			lowercase__					:      List[Any]      =		self.tokenizer(self.src_text,					return_tensors='tf'			)
			lowercase__					:      Dict      =		self.model.generate(
			    model_inputs.input_ids,					attention_mask=model_inputs.attention_mask,					num_beams=2,					use_cache=snake_case__,					)
			lowercase__					:      str      =		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.",
			)





from collections.abc import Sequence


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Sequence[int] | None = None						)		->				int:
 '''simple docstring'''


 if nums is None or not nums:
  raise ValueError('Input sequence should not be empty'						)

 SCREAMING_SNAKE_CASE		    =      nums[0]
 for i in range(1		,				len(_UpperCamelCase						)						):
  SCREAMING_SNAKE_CASE		    =      nums[i]
  SCREAMING_SNAKE_CASE		    =      max(_UpperCamelCase		,				ans + num		,				_UpperCamelCase						)

 return ans


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Try on a sample input from the user
      a_     :    str     		=   int(input("Enter number of elements : ").strip())
      a_     :    Any     		=   list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
      print(max_subsequence_sum(array))

