



'''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        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= BlenderbotSmallConfig
	lowerCamelCase__    				= {}
	lowerCamelCase__    				= """gelu"""







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

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







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

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

								UpperCAmelCase				: int											=	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				: Optional[Any]											=	prepare_blenderbot_small_inputs_dict(__snake_case	,    __snake_case	,    __snake_case	)
								return config, inputs_dict







	def   A			(		self    :  List[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]	)    ->	str:
								UpperCAmelCase				: str											=	TFBlenderbotSmallModel(config=__snake_case	).get_decoder()
								UpperCAmelCase				: int											=	inputs_dict['''input_ids''']

								UpperCAmelCase				: Optional[int]											=	input_ids[:1, :]
								UpperCAmelCase				: int											=	inputs_dict['''attention_mask'''][:1, :]
								UpperCAmelCase				: Optional[Any]											=	inputs_dict['''head_mask''']
								UpperCAmelCase				: Dict											=	1

								# first forward pass
								UpperCAmelCase				: Optional[Any]											=	model(__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	,    use_cache=__snake_case	)

								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	outputs.to_tuple()

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

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

								UpperCAmelCase				: str											=	model(__snake_case	,    attention_mask=__snake_case	)[0]
								UpperCAmelCase				: 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
								UpperCAmelCase				: List[Any]											=	int(ids_tensor((1,)	,    output_from_past.shape[-1]	)	)
								UpperCAmelCase				: Optional[int]											=	output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase				: Optional[Any]											=	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 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[Any]=None     ,   _lowerCAmelCase						:		int=None     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		str=None     ,   )   ->     Optional[Any]:
							if attention_mask is None:
														UpperCAmelCase				: List[Any]											=	tf.cast(tf.math.not_equal(_lowerCAmelCase     ,   config.pad_token_id						)     ,   tf.inta						)
							if decoder_attention_mask is None:
														UpperCAmelCase				: Tuple											=	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				: int											=	tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
							if decoder_head_mask is None:
														UpperCAmelCase				: Dict											=	tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
							if cross_attn_head_mask is None:
														UpperCAmelCase				: str											=	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"""






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







	def   A			(		self    :  int	)    ->	Any:
								UpperCAmelCase				: Union[str, Any]											=	TFBlenderbotSmallModelTester(self	)
								UpperCAmelCase				: Dict											=	ConfigTester(self	,    config_class=__snake_case	)







	def   A			(		self    :  List[str]	)    ->	Any:
								self.config_tester.run_common_tests()







	def   A			(		self    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: Optional[int]											=	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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= [
	    """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?"""
	]
	lowerCamelCase__    				= """facebook/blenderbot_small-90M"""







	@cached_property
	def   A			(		self    :  str	)    ->	Optional[int]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''	)







	@cached_property
	def   A			(		self    :  Tuple	)    ->	Optional[int]:
								UpperCAmelCase				: List[str]											=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
								return model







	@slow
	def   A			(		self    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: List[Any]											=	self.tokenizer(self.src_text	,    return_tensors='''tf'''	)
								UpperCAmelCase				: Union[str, Any]											=	self.model.generate(
								    model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    num_beams=2	,    use_cache=__snake_case	,    )
								UpperCAmelCase				: List[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.",
								)










'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

UpperCamelCase__:     Tuple										=    [8, 5, 9, 7]
UpperCamelCase__:     int										=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__:     List[str]										=    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  list[int]	,    __snake_case    :  list[list[int]]	,    __snake_case    :  list[list[int]]	,    )    ->	None:
								UpperCAmelCase				: Union[str, Any]											=	claim_vector
								UpperCAmelCase				: Tuple											=	allocated_resources_table
								UpperCAmelCase				: List[str]											=	maximum_claim_table







	def   A			(		self    :  Optional[Any]	)    ->	list[int]:
								return [
								    sum(p_item[i] for p_item in self.__allocated_resources_table	)
								    for i in range(len(self.__allocated_resources_table[0]	)	)
								]







	def   A			(		self    :  Union[str, Any]	)    ->	list[int]:
								return np.array(self.__claim_vector	) - np.array(
								    self.__processes_resource_summation()	)







	def   A			(		self    :  str	)    ->	list[list[int]]:
								return [
								    list(np.array(self.__maximum_claim_table[i]	) - np.array(__snake_case	)	)
								    for i, allocated_resource in enumerate(self.__allocated_resources_table	)
								]







	def   A			(		self    :  List[Any]	)    ->	dict[int, list[int]]:
								return {self.__need().index(__snake_case	): i for i in self.__need()}







	def   A			(		self    :  Tuple	,    **__snake_case    :  Union[str, Any]	)    ->	None:
								UpperCAmelCase				: Tuple											=	self.__need()
								UpperCAmelCase				: Union[str, Any]											=	self.__allocated_resources_table
								UpperCAmelCase				: int											=	self.__available_resources()
								UpperCAmelCase				: Tuple											=	self.__need_index_manager()
								for kw, val in kwargs.items():
															if kw and val is True:
																						self.__pretty_data()
								print('''_''' * 50 + '''\n'''	)
								while need_list:
															UpperCAmelCase				: Dict											=	False
															for each_need in need_list:
																						UpperCAmelCase				: Optional[int]											=	True
																						for index, need in enumerate(__snake_case	):
																													if need > available_resources[index]:
																																				UpperCAmelCase				: List[str]											=	False
																																				break
																						if execution:
																													UpperCAmelCase				: str											=	True
																													# get the original index of the process from ind_ctrl db
																													for original_need_index, need_clone in need_index_manager.items():
																																				if each_need == need_clone:
																																											UpperCAmelCase				: Union[str, Any]											=	original_need_index
																													print(F"""Process {process_number + 1} is executing."""	)
																													# remove the process run from stack
																													need_list.remove(__snake_case	)
																													# update available/freed resources stack
																													UpperCAmelCase				: Optional[int]											=	np.array(__snake_case	) + np.array(
																													    alloc_resources_table[process_number]	)
																													print(
																													    '''Updated available resource stack for processes: '''
																													    + ''' '''.join([str(__snake_case	) for x in available_resources]	)	)
																													break
															if safe:
																						print('''The process is in a safe state.\n'''	)
															else:
																						print('''System in unsafe state. Aborting...\n'''	)
																						break







	def   A			(		self    :  Dict	)    ->	Optional[Any]:
								print(''' ''' * 9 + '''Allocated Resource Table'''	)
								for item in self.__allocated_resources_table:
															print(
															    F"""P{self.__allocated_resources_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(''' ''' * 9 + '''System Resource Table'''	)
								for item in self.__maximum_claim_table:
															print(
															    F"""P{self.__maximum_claim_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(
								    '''Current Usage by Active Processes: '''
								    + ''' '''.join(str(__snake_case	) for x in self.__claim_vector	)	)
								print(
								    '''Initial Available Resources:       '''
								    + ''' '''.join(str(__snake_case	) for x in self.__available_resources()	)	)
								time.sleep(1	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





