

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase_			      =      '''platform'''

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)


def     __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    ,						SCREAMING_SNAKE_CASE__=None    ,						):
						if attention_mask is None:
												snake_case_  =      np.where(input_ids != config.pad_token_id    ,						1    ,						0			)
						if decoder_attention_mask is None:
												snake_case_  =      np.where(decoder_input_ids != config.pad_token_id    ,						1    ,						0			)
						if head_mask is None:
												snake_case_  =      np.ones((config.encoder_layers, config.encoder_attention_heads)			)
						if decoder_head_mask is None:
												snake_case_  =      np.ones((config.decoder_layers, config.decoder_attention_heads)			)
						if cross_attn_head_mask is None:
												snake_case_  =      np.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": attention_mask,
						}





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Tuple				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Tuple=1_3				,  _UpperCamelCase					:							Union[str, Any]=7				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							Tuple=False				,  _UpperCamelCase					:							str=9_9				,  _UpperCamelCase					:							List[str]=1_6				,  _UpperCamelCase					:							Any=2				,  _UpperCamelCase					:							List[str]=4				,  _UpperCamelCase					:							Union[str, Any]=4				,  _UpperCamelCase					:							Tuple="gelu"				,  _UpperCamelCase					:							List[str]=0.1				,  _UpperCamelCase					:							int=0.1				,  _UpperCamelCase					:							Union[str, Any]=3_2				,  _UpperCamelCase					:							int=2				,  _UpperCamelCase					:							Optional[int]=1				,  _UpperCamelCase					:							Any=0				,  _UpperCamelCase					:							Union[str, Any]=0.02				,  ) ->Union[str, Any]:
									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_act
									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
									snake_case_  =      initializer_range







			def 							snake_case__(	self					:							Union[str, Any]				) ->Optional[int]:
									snake_case_  =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,  self.vocab_size				)				,  3				,  self.vocab_size				)
									snake_case_  =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,  dtype=np.intaa				))				,  -1				)

									snake_case_  =      shift_tokens_right(_UpperCamelCase				,  1				,  2				)

									snake_case_  =      BlenderbotConfig(
									    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_id=self.eos_token_id				,  bos_token_id=self.bos_token_id				,  pad_token_id=self.pad_token_id				,  initializer_range=self.initializer_range				,  use_cache=_UpperCamelCase				,  )
									snake_case_  =      prepare_blenderbot_inputs_dict(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)
									return config, inputs_dict







			def 							snake_case__(	self					:							List[Any]				) ->int:
									snake_case_,       snake_case_  =      self.prepare_config_and_inputs()
									return config, inputs_dict







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Optional[Any]				) ->Optional[int]:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

									snake_case_  =      model.encode(inputs_dict['''input_ids''']				)

									snake_case_,       snake_case_  =      (
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									snake_case_  =      model.init_cache(decoder_input_ids.shape[0]				,  _UpperCamelCase				,  _UpperCamelCase				)
									snake_case_  =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''				)

									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,  )
									snake_case_  =      model.decode(
									    decoder_input_ids[:, :-1]				,  _UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				,  past_key_values=_UpperCamelCase				,  decoder_position_ids=_UpperCamelCase				,  )

									snake_case_  =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model.decode(
									    decoder_input_ids[:, -1:]				,  _UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				,  past_key_values=outputs_cache.past_key_values				,  decoder_position_ids=_UpperCamelCase				,  )

									snake_case_  =      model.decode(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[Any]				) ->Tuple:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

									snake_case_  =      model.encode(inputs_dict['''input_ids''']				)

									snake_case_,       snake_case_  =      (
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									snake_case_  =      jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
									    ]				,  axis=-1				,  )

									snake_case_  =      model.init_cache(decoder_input_ids.shape[0]				,  _UpperCamelCase				,  _UpperCamelCase				)
									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,  )

									snake_case_  =      model.decode(
									    decoder_input_ids[:, :-1]				,  _UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				,  past_key_values=_UpperCamelCase				,  decoder_position_ids=_UpperCamelCase				,  )
									snake_case_  =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model.decode(
									    decoder_input_ids[:, -1:]				,  _UpperCamelCase				,  past_key_values=outputs_cache.past_key_values				,  decoder_attention_mask=_UpperCamelCase				,  decoder_position_ids=_UpperCamelCase				,  )

									snake_case_  =      model.decode(_UpperCamelCase				,  _UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)






@require_flax
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							99







			def 							snake_case__(	self					:							Tuple				) ->Any:
									snake_case_  =      np.array(
									    [
									        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
									        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
									        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
									        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
									        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
									        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
									        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
									        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
									        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
									        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
									        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
									        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
									        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
									    ]				,  dtype=np.intaa				,  )

									snake_case_  =      input_ids.shape[0]
									snake_case_  =      BlenderbotConfig(
									    vocab_size=self.vocab_size				,  d_model=2_4				,  encoder_layers=2				,  decoder_layers=2				,  encoder_attention_heads=2				,  decoder_attention_heads=2				,  encoder_ffn_dim=3_2				,  decoder_ffn_dim=3_2				,  max_position_embeddings=4_8				,  eos_token_id=2				,  pad_token_id=1				,  bos_token_id=0				,  )
									return config, input_ids, batch_size







			def 							snake_case__(	self					:							Any				) ->Tuple:
									snake_case_,       snake_case_,       snake_case_  =      self._get_config_and_data()
									snake_case_  =      FlaxBlenderbotForConditionalGeneration(_UpperCamelCase				)
									snake_case_  =      lm_model(input_ids=_UpperCamelCase				)
									snake_case_  =      (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[Any]				) ->Optional[Any]:
									snake_case_  =      BlenderbotConfig(
									    vocab_size=self.vocab_size				,  d_model=1_4				,  encoder_layers=2				,  decoder_layers=2				,  encoder_attention_heads=2				,  decoder_attention_heads=2				,  encoder_ffn_dim=8				,  decoder_ffn_dim=8				,  max_position_embeddings=4_8				,  )
									snake_case_  =      FlaxBlenderbotForConditionalGeneration(_UpperCamelCase				)
									snake_case_  =      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,  dtype=np.intaa				)
									snake_case_  =      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,  dtype=np.intaa				)
									snake_case_  =      lm_model(input_ids=_UpperCamelCase				,  decoder_input_ids=_UpperCamelCase				)
									snake_case_  =      (*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							Tuple				) ->List[str]:
									snake_case_  =      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,  dtype=np.intaa				)
									snake_case_  =      shift_tokens_right(_UpperCamelCase				,  1				,  2				)
									snake_case_  =      np.equal(_UpperCamelCase				,  1				).astype(np.floataa				).sum()
									snake_case_  =      np.equal(_UpperCamelCase				,  1				).astype(np.floataa				).sum()
									self.assertEqual(shifted.shape				,  input_ids.shape				)
									self.assertEqual(_UpperCamelCase				,  n_pad_before - 1				)
									self.assertTrue(np.equal(shifted[:, 0]				,  2				).all()				)






@require_flax
class       snake_case_  (						__A      ,			unittest.TestCase      ,			__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  int		     =							True
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							(
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE	:  List[str]		     =							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







			def 							snake_case__(	self					:							List[str]				) ->Dict:
									snake_case_  =      FlaxBlenderbotModelTester(self				)







			def 							snake_case__(	self					:							int				) ->Any:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->List[Any]:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[Any]				) ->Optional[int]:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					snake_case_  =      self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)
																					snake_case_  =      model_class(_UpperCamelCase				)

																					@jax.jit
																					def encode_jitted(_UpperCamelCase					:							str				,  _UpperCamelCase					:							Union[str, Any]=None				,  **_UpperCamelCase					:							List[str]				):
																											return model.encode(input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

																					with self.subTest('''JIT Enabled'''				):
																											snake_case_  =      encode_jitted(**_UpperCamelCase				).to_tuple()

																					with self.subTest('''JIT Disabled'''				):
																											with jax.disable_jit():
																																	snake_case_  =      encode_jitted(**_UpperCamelCase				).to_tuple()

																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				)
																					for jitted_output, output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assertEqual(jitted_output.shape				,  output.shape				)







			def 							snake_case__(	self					:							List[str]				) ->Any:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					snake_case_  =      model_class(_UpperCamelCase				)
																					snake_case_  =      model.encode(inputs_dict['''input_ids''']				,  inputs_dict['''attention_mask''']				)

																					snake_case_  =      {
																					    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																					    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																					    '''encoder_outputs''': encoder_outputs,
																					}

																					@jax.jit
																					def decode_jitted(_UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							List[Any]				):
																											return model.decode(
																											    decoder_input_ids=_UpperCamelCase				,  decoder_attention_mask=_UpperCamelCase				,  encoder_outputs=_UpperCamelCase				,  )

																					with self.subTest('''JIT Enabled'''				):
																											snake_case_  =      decode_jitted(**_UpperCamelCase				).to_tuple()

																					with self.subTest('''JIT Disabled'''				):
																											with jax.disable_jit():
																																	snake_case_  =      decode_jitted(**_UpperCamelCase				).to_tuple()

																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				)
																					for jitted_output, output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assertEqual(jitted_output.shape				,  output.shape				)







			@slow
			def 							snake_case__(	self					:							Dict				) ->Optional[Any]:
									for model_class_name in self.all_model_classes:
															snake_case_  =      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															snake_case_  =      np.ones((1, 1)				) * model.config.eos_token_id
															snake_case_  =      model(_UpperCamelCase				)
															self.assertIsNotNone(_UpperCamelCase				)







			@unittest.skipUnless(jax_device != '''cpu'''				,  '''3B test too slow on CPU.'''				)
			@slow
			def 							snake_case__(	self					:							Optional[Any]				) ->int:
									snake_case_  =      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
									snake_case_  =      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

									snake_case_  =      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''				,  from_pt=_UpperCamelCase				)
									snake_case_  =      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

									snake_case_  =      ['''Sam''']
									snake_case_  =      tokenizer(_UpperCamelCase				,  return_tensors='''jax'''				)

									snake_case_  =      model.generate(**_UpperCamelCase				,  **_UpperCamelCase				)
									snake_case_  =      '''Sam is a great name. It means "sun" in Gaelic.'''

									snake_case_  =      tokenizer.batch_decode(_UpperCamelCase				,  **_UpperCamelCase				)
									assert generated_txt[0].strip() == tgt_text


from math import sqrt





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5  ,				int(sqrt(UpperCamelCase__ ) + 1 )  ,				6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True





def    __lowerCamelCase      (					UpperCamelCase__ = 10001 ):







							'''simple docstring'''






							snake_case_     							=       0
							snake_case_     							=       1
							while count != nth and number < 3:
														number += 1
														if is_prime(UpperCamelCase__ ):
																					count += 1
							while count != nth:
														number += 2
														if is_prime(UpperCamelCase__ ):
																					count += 1
							return number


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




