




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 :
			snake_case_      : Tuple      =     BlenderbotSmallConfig
			snake_case_      : Any      =     {}
			snake_case_      : List[str]      =     "gelu"







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




									"""simple docstring"""


									_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 UpperCamelCase			(       self    :			List[str]      ):




									"""simple docstring"""


									_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(snake_case__    ,    snake_case__    ,    snake_case__      )
									return config, inputs_dict







			def UpperCamelCase			(       self    :			Optional[int]    ,    snake_case__    :			str    ,    snake_case__    :			int      ):




									"""simple docstring"""


									_UpperCAmelCase													=     TFBlenderbotSmallModel(config=snake_case__      ).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(snake_case__    ,    attention_mask=snake_case__    ,    head_mask=snake_case__    ,    use_cache=snake_case__      )

									_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(snake_case__    ,    attention_mask=snake_case__      )[0]
									_UpperCAmelCase													=     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													=     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(snake_case__    ,    snake_case__    ,    rtol=1e-3      )




def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_	,    snake_case_=None	,    snake_case_=None	,    snake_case_=None	,    snake_case_=None	,    snake_case_=None	,    ):

						'''simple docstring'''


						if attention_mask is None:
												_UpperCAmelCase													=     tf.cast(tf.math.not_equal(_lowercase	,    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     __lowerCAmelCase (       UpperCAmelCase__					,		UpperCAmelCase__					,		unittest.TestCase ):
			snake_case_      : List[str]      =     (
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			snake_case_      : int      =     (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			snake_case_      : Optional[Any]      =     (
			    {
			        "conversational": TFBlenderbotSmallForConditionalGeneration,
			        "feature-extraction": TFBlenderbotSmallModel,
			        "summarization": TFBlenderbotSmallForConditionalGeneration,
			        "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
			        "translation": TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case_      : Optional[int]      =     True
			snake_case_      : List[Any]      =     False
			snake_case_      : Any      =     False







			def UpperCamelCase			(       self    :			Optional[int]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     TFBlenderbotSmallModelTester(self      )
									_UpperCAmelCase													=     ConfigTester(self    ,    config_class=snake_case__      )







			def UpperCamelCase			(       self    :			Dict      ):




									"""simple docstring"""


									self.config_tester.run_common_tests()







			def UpperCamelCase			(       self    :			str      ):




									"""simple docstring"""


									_UpperCAmelCase													=     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 ):
			snake_case_      : Optional[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_      : Optional[Any]      =     "facebook/blenderbot_small-90M"







			@cached_property
			def UpperCamelCase			(       self    :			Optional[int]      ):




									"""simple docstring"""


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







			@cached_property
			def UpperCamelCase			(       self    :			List[str]      ):




									"""simple docstring"""


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







			@slow
			def UpperCamelCase			(       self    :			int      ):




									"""simple docstring"""


									_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=snake_case__    ,    )
									_UpperCAmelCase													=     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 itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

