
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__					:





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = BlenderbotSmallConfig
	_UpperCAmelCase	:		Union[str, Any]          = {}
	_UpperCAmelCase	:		str          = "gelu"



	def __init__(  self     :   Any      ,  lowercase     :   Dict      ,  lowercase     :   Dict=13      ,  lowercase     :   Optional[Any]=7      ,  lowercase     :   Any=True      ,  lowercase     :   Any=False      ,  lowercase     :   Optional[Any]=99      ,  lowercase     :   int=32      ,  lowercase     :   Optional[Any]=2      ,  lowercase     :   str=4      ,  lowercase     :   str=37      ,  lowercase     :   List[str]=0.1      ,  lowercase     :   List[Any]=0.1      ,  lowercase     :   int=20      ,  lowercase     :   Optional[int]=2      ,  lowercase     :   Union[str, Any]=1      ,  lowercase     :   List[Any]=0      ,  ):


				'''simple docstring'''


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

				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      max_position_embeddings
				_snake_case          =      eos_token_id
				_snake_case          =      pad_token_id
				_snake_case          =      bos_token_id



	def 			A							(  self     :   str     ):


				'''simple docstring'''


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

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

				_snake_case          =      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      ,  )
				_snake_case          =      prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE     )
				return config, inputs_dict





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


				'''simple docstring'''


				_snake_case          =      TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE     ).get_decoder()
				_snake_case          =      inputs_dict['input_ids']

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

				# first forward pass
				_snake_case          =      model(__SCREAMING_SNAKE_CASE      ,  attention_mask=__SCREAMING_SNAKE_CASE      ,  head_mask=__SCREAMING_SNAKE_CASE      ,  use_cache=__SCREAMING_SNAKE_CASE     )

				_snake_case     , _snake_case          =      outputs.to_tuple()

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

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

				_snake_case          =      model(__SCREAMING_SNAKE_CASE      ,  attention_mask=__SCREAMING_SNAKE_CASE     )[0]
				_snake_case          =      model(__SCREAMING_SNAKE_CASE      ,  attention_mask=__SCREAMING_SNAKE_CASE      ,  past_key_values=__SCREAMING_SNAKE_CASE     )[0]

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

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

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






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Union[str, Any]	, __lowercase       :     Any	, __lowercase       :     int=None	, __lowercase       :     List[Any]=None	, __lowercase       :     Optional[Any]=None	, __lowercase       :     List[Any]=None	, __lowercase       :     Any=None	, )			->  Optional[int]:
			if attention_mask is None:
						_snake_case          =      tf.cast(tf.math.not_equal(lowercase__	, config.pad_token_id			)	, tf.inta			)
			if decoder_attention_mask is None:
						_snake_case          =      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:
						_snake_case          =      tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						_snake_case          =      tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						_snake_case          =      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__					(     _A							,_A							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = (
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	_UpperCAmelCase	:		Dict          = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	_UpperCAmelCase	:		Dict          = (
	    {
	        "conversational": TFBlenderbotSmallForConditionalGeneration,
	        "feature-extraction": TFBlenderbotSmallModel,
	        "summarization": TFBlenderbotSmallForConditionalGeneration,
	        "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
	        "translation": TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	_UpperCAmelCase	:		Optional[Any]          = True
	_UpperCAmelCase	:		str          = False
	_UpperCAmelCase	:		Tuple          = False



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      TFBlenderbotSmallModelTester(self     )
				_snake_case          =      ConfigTester(self      ,  config_class=__SCREAMING_SNAKE_CASE     )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				self.config_tester.run_common_tests()





	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE     )




@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	_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	:		Optional[int]          = "facebook/blenderbot_small-90M"



	@cached_property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


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



	@cached_property
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


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





	@slow
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer(self.src_text      ,  return_tensors='tf'     )
				_snake_case          =      self.model.generate(
				    model_inputs.input_ids      ,  attention_mask=model_inputs.attention_mask      ,  num_beams=2      ,  use_cache=__SCREAMING_SNAKE_CASE      ,  )
				_snake_case          =      self.tokenizer.batch_decode(generated_ids.numpy()      ,  skip_special_tokens=__SCREAMING_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 __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)