



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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__		:					Any							='''platform'''

			import jax
			import jax.numpy as jnp

			from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
			    FlaxBlenderbotSmallForConditionalGeneration,
			    FlaxBlenderbotSmallModel,
			    shift_tokens_right,
			)






def      __lowercase       (			a__							,       a__							,       a__=None							,       a__=None							,       a__=None							,       a__=None							,       a__=None							,       a__=None							,       )    ->			Dict:
							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        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=13				,							_A=7				,							_A=True				,							_A=False				,							_A=99				,							_A=16				,							_A=2				,							_A=4				,							_A=4				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=32				,							_A=2				,							_A=1				,							_A=0				,							_A=0.0_2				,							):







													'''simple docstring'''

													__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		):







													'''simple docstring'''

													__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(_A				,							1				,							2		)

													__SCREAMING_SNAKE_CASE									= BlenderbotSmallConfig(
													    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=_A				,							)
													__SCREAMING_SNAKE_CASE									= prepare_blenderbot_inputs_dict(_A				,							_A				,							_A		)
													return config, inputs_dict






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.prepare_config_and_inputs()
													return config, inputs_dict






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 20
													__SCREAMING_SNAKE_CASE									= model_class_name(_A		)

													__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]				,							_A				,							_A		)
													__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]				,							_A				,							decoder_attention_mask=_A				,							past_key_values=_A				,							decoder_position_ids=_A				,							)

													__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:]				,							_A				,							decoder_attention_mask=_A				,							past_key_values=outputs_cache.past_key_values				,							decoder_position_ids=_A				,							)

													__SCREAMING_SNAKE_CASE									= model.decode(_A				,							_A		)

													__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				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 20
													__SCREAMING_SNAKE_CASE									= model_class_name(_A		)

													__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]				,							_A				,							_A		)
													__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]				,							_A				,							decoder_attention_mask=_A				,							past_key_values=_A				,							decoder_position_ids=_A				,							)
													__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:]				,							_A				,							past_key_values=outputs_cache.past_key_values				,							decoder_attention_mask=_A				,							decoder_position_ids=_A				,							)

													__SCREAMING_SNAKE_CASE									= model.decode(_A				,							_A				,							decoder_attention_mask=_A		)

													__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        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[Any]      =  99






						def 	_A     (   self		):







													'''simple docstring'''

													__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									= BlenderbotSmallConfig(
													    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		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self._get_config_and_data()
													__SCREAMING_SNAKE_CASE									= FlaxBlenderbotSmallForConditionalGeneration(_A		)
													__SCREAMING_SNAKE_CASE									= lm_model(input_ids=_A		)
													__SCREAMING_SNAKE_CASE									= (batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs['logits'].shape				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= BlenderbotSmallConfig(
													    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									= FlaxBlenderbotSmallForConditionalGeneration(_A		)
													__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=_A				,							decoder_input_ids=_A		)
													__SCREAMING_SNAKE_CASE									= (*summary.shape, config.vocab_size)
													self.assertEqual(outputs['logits'].shape				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							1				,							2		)
													__SCREAMING_SNAKE_CASE									= np.equal(_A				,							1		).astype(np.floataa		).sum()
													__SCREAMING_SNAKE_CASE									= np.equal(_A				,							1		).astype(np.floataa		).sum()
													self.assertEqual(shifted.shape				,							input_ids.shape		)
													self.assertEqual(_A				,							n_pad_before - 1		)
													self.assertTrue(np.equal(shifted[:, 0]				,							2		).all()		)







@require_flax
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase   ,   UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			List[str]      =  (
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase__      :			Dict      =  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= FlaxBlenderbotSmallModelTester(self		)






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							_A				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							_A				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							_A		)
																											__SCREAMING_SNAKE_CASE									= model_class(_A		)

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

																											with self.subTest('JIT Enabled'		):
																																		__SCREAMING_SNAKE_CASE									= encode_jitted(**_A		).to_tuple()

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

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






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A		)
																											__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(_A				,							_A				,							_A		):
																																		return model.decode(
																																		    decoder_input_ids=_A				,							decoder_attention_mask=_A				,							encoder_outputs=_A				,							)

																											with self.subTest('JIT Enabled'		):
																																		__SCREAMING_SNAKE_CASE									= decode_jitted(**_A		).to_tuple()

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

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






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													for model_class_name in self.all_model_classes:
																				__SCREAMING_SNAKE_CASE									= model_class_name.from_pretrained('facebook/blenderbot_small-90M'		)
																				# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																				__SCREAMING_SNAKE_CASE									= np.ones((1, 1)		) * model.config.eos_token_id
																				__SCREAMING_SNAKE_CASE									= model(_A		)
																				self.assertIsNotNone(_A		)











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






