


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 	A_ :




						'''simple docstring'''


						__snake_case 	=       BlenderbotConfig
						__snake_case 	=       {}
						__snake_case 	=       """gelu"""




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

							__lowerCamelCase		:							Dict		 =						hidden_dropout_prob
							__lowerCamelCase		:							List[Any]		 =						attention_probs_dropout_prob
							__lowerCamelCase		:							Tuple		 =						max_position_embeddings
							__lowerCamelCase		:							str		 =						eos_token_id
							__lowerCamelCase		:							Optional[int]		 =						pad_token_id
							__lowerCamelCase		:							Dict		 =						bos_token_id




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

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

							__lowerCamelCase		:							List[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						,				)
							__lowerCamelCase		:							Tuple		 =						prepare_blenderbot_inputs_dict(lowercase_						,				lowercase_						,				lowercase_    )
							return config, inputs_dict




						def 					_snake_case     (    self:	int						,				a:	str						,				a:	str    ):
							__lowerCamelCase		:							Optional[int]		 =						TFBlenderbotModel(config=lowercase_    ).get_decoder()
							__lowerCamelCase		:							Union[str, Any]		 =						inputs_dict['input_ids']

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

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

							__lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						outputs.to_tuple()

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

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

							__lowerCamelCase		:							int		 =						model(lowercase_						,				attention_mask=lowercase_    )[0]
							__lowerCamelCase		:							List[Any]		 =						model(lowercase_						,				attention_mask=lowercase_						,				past_key_values=lowercase_    )[0]

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

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

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



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

	if attention_mask is None:
		__lowerCamelCase		:							List[str]		 =						tf.cast(tf.math.not_equal(__UpperCAmelCase      ,	config.pad_token_id						)      ,	tf.inta						)
	if decoder_attention_mask is None:
		__lowerCamelCase		:							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:
		__lowerCamelCase		:							int		 =						tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
	if decoder_head_mask is None:
		__lowerCamelCase		:							List[str]		 =						tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
	if cross_attn_head_mask is None:
		__lowerCamelCase		:							Union[str, 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 	A_ (	_lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase   ):




						'''simple docstring'''


						__snake_case 	=       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
						__snake_case 	=       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
						__snake_case 	=       (
						    {
						        """conversational""": TFBlenderbotForConditionalGeneration,
						        """feature-extraction""": TFBlenderbotModel,
						        """summarization""": TFBlenderbotForConditionalGeneration,
						        """text2text-generation""": TFBlenderbotForConditionalGeneration,
						        """translation""": TFBlenderbotForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						__snake_case 	=       True
						__snake_case 	=       False
						__snake_case 	=       False




						def 					_snake_case     (    self:	str    ):
							__lowerCamelCase		:							List[Any]		 =						TFBlenderbotModelTester(self    )
							__lowerCamelCase		:							Union[str, Any]		 =						ConfigTester(self						,				config_class=lowercase_    )




						def 					_snake_case     (    self:	Optional[Any]    ):
							self.config_tester.run_common_tests()




						def 					_snake_case     (    self:	int    ):
							__lowerCamelCase		:							Optional[int]		 =						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*lowercase_    )



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




						'''simple docstring'''


						__snake_case 	=       ["""My friends are cool but they eat too many carbs."""]
						__snake_case 	=       """facebook/blenderbot-400M-distill"""




						@cached_property
						def 					_snake_case     (    self:	Optional[Any]    ):
							return BlenderbotTokenizer.from_pretrained(self.model_name    )




						@cached_property
						def 					_snake_case     (    self:	str    ):
							__lowerCamelCase		:							List[Any]		 =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
							return model




						@slow
						def 					_snake_case     (    self:	int    ):
							__lowerCamelCase		:							Optional[int]		 =						self.tokenizer(self.src_text						,				return_tensors='tf'    )
							__lowerCamelCase		:							int		 =						self.model.generate(
							    model_inputs.input_ids						,				)
							__lowerCamelCase		:							Union[str, Any]		 =						self.tokenizer.batch_decode(generated_ids.numpy()						,				skip_special_tokens=lowercase_    )[0]
							assert (
							    generated_words
							    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
							)





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1						):
  __lowerCamelCase		:							Dict		 =						True

 # sum is not zero and set is empty then false
 for i in range(1      ,	required_sum + 1						):
  __lowerCamelCase		:							Union[str, Any]		 =						False

 for i in range(1      ,	arr_len + 1						):
  for j in range(1      ,	required_sum + 1						):
   if arr[i - 1] > j:
    __lowerCamelCase		:							Tuple		 =						subset[i - 1][j]
   if arr[i - 1] <= j:
    __lowerCamelCase		:							List[Any]		 =						subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

