






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 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   Optional[Any]						=					BlenderbotSmallConfig
						A     :   str						=					{}
						A     :   Optional[int]						=					'gelu'






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

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






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

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

								snake_case_				:							Optional[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		,				)
								snake_case_				:							Optional[Any]						 =       prepare_blenderbot_small_inputs_dict(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								return config, inputs_dict






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       TFBlenderbotSmallModel(config=_SCREAMING_SNAKE_CASE				).get_decoder()
								snake_case_				:							Dict						 =       inputs_dict["input_ids"]

								snake_case_				:							int						 =       input_ids[:1, :]
								snake_case_				:							Optional[Any]						 =       inputs_dict["attention_mask"][:1, :]
								snake_case_				:							Union[str, Any]						 =       inputs_dict["head_mask"]
								snake_case_				:							Optional[Any]						 =       1

								# first forward pass
								snake_case_				:							int						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				head_mask=_SCREAMING_SNAKE_CASE		,				use_cache=_SCREAMING_SNAKE_CASE				)

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

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

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

								snake_case_				:							Dict						 =       model(_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE				)[0]
								snake_case_				:							List[Any]						 =       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_				:							List[Any]						 =       int(ids_tensor((1,)		,				output_from_past.shape[-1]				)				)
								snake_case_				:							List[Any]						 =       output_from_no_past[:, -3:, random_slice_idx]
								snake_case_				:							Union[str, Any]						 =       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      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int  ,      _a       :			Union[str, Any]  ,      _a       :			List[Any]=None  ,      _a       :			List[Any]=None  ,      _a       :			Any=None  ,      _a       :			Dict=None  ,      _a       :			Optional[int]=None  ,      ):
		if attention_mask is None:
				snake_case_				:							Union[str, Any]						 =       tf.cast(tf.math.not_equal(_a  ,      config.pad_token_id    )  ,      tf.inta    )
		if decoder_attention_mask is None:
				snake_case_				:							Optional[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:
				snake_case_				:							str						 =       tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				snake_case_				:							Dict						 =       tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				snake_case_				:							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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Any						=					(
						    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
						)
						A     :   str						=					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
						A     :   str						=					(
						    {
						        'conversational': TFBlenderbotSmallForConditionalGeneration,
						        'feature-extraction': TFBlenderbotSmallModel,
						        'summarization': TFBlenderbotSmallForConditionalGeneration,
						        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
						        'translation': TFBlenderbotSmallForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						A     :   List[str]						=					True
						A     :   Tuple						=					False
						A     :   Any						=					False






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							int						 =       TFBlenderbotSmallModelTester(self				)
								snake_case_				:							List[Any]						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE				)






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






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       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 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						A     :   List[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?'
						]
						A     :   Tuple						=					'facebook/blenderbot_small-90M'






						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Any:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"				)






						@cached_property
						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Union[str, Any]						 =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
								return model






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Union[str, Any]						 =       self.tokenizer(self.src_text		,				return_tensors="tf"				)
								snake_case_				:							List[str]						 =       self.model.generate(
								    model_inputs.input_ids		,				attention_mask=model_inputs.attention_mask		,				num_beams=2		,				use_cache=_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       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

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase  :							Optional[Any]			  =    2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowercase  :							Optional[Any]			  =    50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowercase  :							Union[str, Any]			  =    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Union[str, Any]						 =       len([g for position, g in enumerate(_a    ) if g == main_target[position]]    )
		return (item, float(_a    ))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Tuple						 =       random.randint(0  ,      len(_a    ) - 1    )
		snake_case_				:							Any						 =       parent_a[:random_slice] + parent_a[random_slice:]
		snake_case_				:							Optional[Any]						 =       parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]    ):
		snake_case_				:							str						 =       list(_a    )
		if random.uniform(0  ,      1    ) < MUTATION_PROBABILITY:
				snake_case_				:							Optional[Any]						 =       random.choice(_a    )
		return "".join(_a    )

def      lowerCAmelCase__	(    _a       :			tuple[str, float]  ,      _a       :			list[tuple[str, float]]  ,      _a       :			list[str]  ,      ):
		snake_case_				:							Tuple						 =       []
		# Generate more children proportionally to the fitness score.
		snake_case_				:							Optional[int]						 =       int(parent_a[1] * 1_00    ) + 1
		snake_case_				:							Tuple						 =       10 if child_n >= 10 else child_n
		for _ in range(_a    ):
				snake_case_				:							int						 =       population_score[random.randint(0  ,      _a    )][0]

				snake_case_ ,       snake_case_				:							Any						 =       crossover(parent_a[0]  ,      _a    )
				# Append new string to the population list.
				pop.append(mutate(_a  ,      _a    )    )
				pop.append(mutate(_a  ,      _a    )    )
		return pop

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]  ,      _a       :			bool = True    ):

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				snake_case_				:							Any						 =       F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
				raise ValueError(_a    )
		# Verify that the target contains no genes besides the ones inside genes variable.
		snake_case_				:							Optional[Any]						 =       sorted({c for c in target if c not in genes}    )
		if not_in_genes_list:
				snake_case_				:							str						 =       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
				raise ValueError(_a    )

		# Generate random starting population.
		snake_case_				:							Optional[int]						 =       []
		for _ in range(_a    ):
				population.append("".join([random.choice(_a    ) for i in range(len(_a    )    )]    )    )

		# Just some logs to know what the algorithms is doing.
		snake_case_ ,       snake_case_				:							Dict						 =       0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(_a    )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				snake_case_				:							Optional[int]						 =       [evaluate(_a  ,      _a    ) for item in population]

				# Check if there is a matching evolution.
				snake_case_				:							Any						 =       sorted(_a  ,      key=lambda _a    : x[1]  ,      reverse=_a    )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F'''\nGeneration: {generation}'''
						    F'''\nTotal Population:{total_population}'''
						    F'''\nBest score: {population_score[0][1]}'''
						    F'''\nBest string: {population_score[0][0]}'''    )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				snake_case_				:							Union[str, Any]						 =       population[: int(N_POPULATION / 3    )]
				population.clear()
				population.extend(_a    )
				# Normalize population score to be between 0 and 1.
				snake_case_				:							int						 =       [
				    (item, score / len(_a    )) for item, score in population_score
				]

				# This is selection
				for i in range(_a    ):
						population.extend(select(population_score[int(_a    )]  ,      _a  ,      _a    )    )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(_a    ) > N_POPULATION:
								break


if __name__ == "__main__":
							lowercase  :							Dict			  =    (
							    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
							)
							lowercase  :							Optional[int]			  =    list(
							    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
							    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
							)
							lowercase  ,lowercase  ,lowercase  :							Optional[Any]			  =    basic(target_str, genes_list)
							print(
							    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
							)
