




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
			__SCREAMING_SNAKE_CASE     :   Tuple 						="""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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase=None  ,    ):
							if attention_mask is None:
														SCREAMING_SNAKE_CASE_   	=    np.where(input_ids != config.pad_token_id  ,    1  ,    0     )
							if decoder_attention_mask is None:
														SCREAMING_SNAKE_CASE_   	=    np.where(decoder_input_ids != config.pad_token_id  ,    1  ,    0     )
							if head_mask is None:
														SCREAMING_SNAKE_CASE_   	=    np.ones((config.encoder_layers, config.encoder_attention_heads)     )
							if decoder_head_mask is None:
														SCREAMING_SNAKE_CASE_   	=    np.ones((config.decoder_layers, config.decoder_attention_heads)     )
							if cross_attn_head_mask is None:
														SCREAMING_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  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   str		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]=13		,					_lowerCamelCase:   int=7		,					_lowerCamelCase:   List[str]=True		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   int=99		,					_lowerCamelCase:   List[str]=16		,					_lowerCamelCase:   Optional[int]=2		,					_lowerCamelCase:   List[str]=4		,					_lowerCamelCase:   str=4		,					_lowerCamelCase:   int="gelu"		,					_lowerCamelCase:   int=0.1		,					_lowerCamelCase:   str=0.1		,					_lowerCamelCase:   Optional[Any]=32		,					_lowerCamelCase:   Tuple=2		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[Any]=0		,					_lowerCamelCase:   str=0.02		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    seq_length
											SCREAMING_SNAKE_CASE_   	=    is_training
											SCREAMING_SNAKE_CASE_   	=    use_labels
											SCREAMING_SNAKE_CASE_   	=    vocab_size
											SCREAMING_SNAKE_CASE_   	=    hidden_size
											SCREAMING_SNAKE_CASE_   	=    num_hidden_layers
											SCREAMING_SNAKE_CASE_   	=    num_attention_heads
											SCREAMING_SNAKE_CASE_   	=    intermediate_size
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    eos_token_id
											SCREAMING_SNAKE_CASE_   	=    pad_token_id
											SCREAMING_SNAKE_CASE_   	=    bos_token_id
											SCREAMING_SNAKE_CASE_   	=    initializer_range


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,					self.vocab_size		)		,					3		,					self.vocab_size		)
											SCREAMING_SNAKE_CASE_   	=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,					dtype=np.intaa		))		,					-1		)

											SCREAMING_SNAKE_CASE_   	=    shift_tokens_right(_lowerCamelCase		,					1		,					2		)

											SCREAMING_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=_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    prepare_blenderbot_inputs_dict(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)
											return config, inputs_dict


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											return config, inputs_dict


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    20
											SCREAMING_SNAKE_CASE_   	=    model_class_name(_lowerCamelCase		)

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

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

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

											SCREAMING_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)		,					)
											SCREAMING_SNAKE_CASE_   	=    model.decode(
											    decoder_input_ids[:, :-1]		,					_lowerCamelCase		,					decoder_attention_mask=_lowerCamelCase		,					past_key_values=_lowerCamelCase		,					decoder_position_ids=_lowerCamelCase		,					)

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

											SCREAMING_SNAKE_CASE_   	=    model.decode(_lowerCamelCase		,					_lowerCamelCase		)

											SCREAMING_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     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    20
											SCREAMING_SNAKE_CASE_   	=    model_class_name(_lowerCamelCase		)

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

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

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

											SCREAMING_SNAKE_CASE_   	=    model.init_cache(decoder_input_ids.shape[0]		,					_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_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)		,					)

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

											SCREAMING_SNAKE_CASE_   	=    model.decode(_lowerCamelCase		,					_lowerCamelCase		,					decoder_attention_mask=_lowerCamelCase		)

											SCREAMING_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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					99


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        [71, 82, 18, 33, 46, 91, 2],
											        [68, 34, 26, 58, 30, 82, 2],
											        [5, 97, 17, 39, 94, 40, 2],
											        [76, 83, 94, 25, 70, 78, 2],
											        [87, 59, 41, 35, 48, 66, 2],
											        [55, 13, 16, 58, 5, 2, 1],  # note padding
											        [64, 27, 31, 51, 12, 75, 2],
											        [52, 64, 86, 17, 83, 39, 2],
											        [48, 61, 9, 24, 71, 82, 2],
											        [26, 1, 60, 48, 22, 13, 2],
											        [21, 5, 62, 28, 14, 76, 2],
											        [45, 98, 37, 86, 59, 48, 2],
											        [70, 70, 50, 9, 28, 0, 2],
											    ]		,					dtype=np.intaa		,					)

											SCREAMING_SNAKE_CASE_   	=    input_ids.shape[0]
											SCREAMING_SNAKE_CASE_   	=    BlenderbotConfig(
											    vocab_size=self.vocab_size		,					d_model=24		,					encoder_layers=2		,					decoder_layers=2		,					encoder_attention_heads=2		,					decoder_attention_heads=2		,					encoder_ffn_dim=32		,					decoder_ffn_dim=32		,					max_position_embeddings=48		,					eos_token_id=2		,					pad_token_id=1		,					bos_token_id=0		,					)
											return config, input_ids, batch_size


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self._get_config_and_data()
											SCREAMING_SNAKE_CASE_   	=    FlaxBlenderbotForConditionalGeneration(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    lm_model(input_ids=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['''logits'''].shape		,					_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    BlenderbotConfig(
											    vocab_size=self.vocab_size		,					d_model=14		,					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=48		,					)
											SCREAMING_SNAKE_CASE_   	=    FlaxBlenderbotForConditionalGeneration(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,					dtype=np.intaa		)
											SCREAMING_SNAKE_CASE_   	=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,					dtype=np.intaa		)
											SCREAMING_SNAKE_CASE_   	=    lm_model(input_ids=_lowerCamelCase		,					decoder_input_ids=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['''logits'''].shape		,					_lowerCamelCase		)





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,					dtype=np.intaa		)
											SCREAMING_SNAKE_CASE_   	=    shift_tokens_right(_lowerCamelCase		,					1		,					2		)
											SCREAMING_SNAKE_CASE_   	=    np.equal(_lowerCamelCase		,					1		).astype(np.floataa		).sum()
											SCREAMING_SNAKE_CASE_   	=    np.equal(_lowerCamelCase		,					1		).astype(np.floataa		).sum()
											self.assertEqual(shifted.shape		,					input_ids.shape		)
											self.assertEqual(_lowerCamelCase		,					n_pad_before - 1		)
											self.assertTrue(np.equal(shifted[:, 0]		,					2		).all()		)






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase    ,  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Tuple	      =					True
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__					:							str	      =					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    FlaxBlenderbotModelTester(self		)


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_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(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_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__		):
																									SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)

																									@jax.jit
																									def encode_jitted(_lowerCamelCase:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   Tuple		):
																																return model.encode(input_ids=_lowerCamelCase		,					attention_mask=_lowerCamelCase		)

																									with self.subTest('''JIT Enabled'''		):
																																SCREAMING_SNAKE_CASE_   	=    encode_jitted(**_lowerCamelCase		).to_tuple()

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

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


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_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__		):
																									SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    model.encode(inputs_dict['''input_ids''']		,					inputs_dict['''attention_mask''']		)

																									SCREAMING_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(_lowerCamelCase:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Union[str, Any]		):
																																return model.decode(
																																    decoder_input_ids=_lowerCamelCase		,					decoder_attention_mask=_lowerCamelCase		,					encoder_outputs=_lowerCamelCase		,					)

																									with self.subTest('''JIT Enabled'''		):
																																SCREAMING_SNAKE_CASE_   	=    decode_jitted(**_lowerCamelCase		).to_tuple()

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

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


				@slow
				def     _A							(							self:   Dict		):
											for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_   	=    model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		SCREAMING_SNAKE_CASE_   	=    np.ones((1, 1)		) * model.config.eos_token_id
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
																		self.assertIsNotNone(_lowerCamelCase		)





				@unittest.skipUnless(jax_device != '''cpu'''		,					'''3B test too slow on CPU.'''		)
				@slow
				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
											SCREAMING_SNAKE_CASE_   	=    {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

											SCREAMING_SNAKE_CASE_   	=    ['''Sam''']
											SCREAMING_SNAKE_CASE_   	=    tokenizer(_lowerCamelCase		,					return_tensors='''jax'''		)

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

											SCREAMING_SNAKE_CASE_   	=    tokenizer.batch_decode(_lowerCamelCase		,					**_lowerCamelCase		)
											assert generated_txt[0].strip() == tgt_text








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=input("""Enter numbers separated by a comma:\n""").strip()
			__SCREAMING_SNAKE_CASE 						=[int(item) for item in user_input.split(""",""")]
			print(*merge_sort(unsorted), sep=""",""")


