'''simple docstring'''





SCREAMING_SNAKE_CASE_:	dict[str, float]    ={
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609344,
    "knot": 1.852,
}

SCREAMING_SNAKE_CASE_:	dict[str, float]    ={
    "km/h": 1.0,
    "m/s": 0.277777778,
    "mph": 0.621371192,
    "knot": 0.539956803,
}





def       lowerCAmelCase_							(   snake_case_			:					float   ,       snake_case_			:					str   ,       snake_case_			:					str     )       -> float:


     '''simple docstring'''




     if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
          UpperCAmelCase_			       =  (
              f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
              f"""Valid values are: {", ".join(snake_case_     )}"""
          )
          raise ValueError(snake_case_     )
     return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]   ,       3     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()












import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
			UpperCAmelCase__      			=						'''platform'''
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 							snake_case_       :

						"""simple docstring"""



						snake_case__        =      PegasusConfig
						snake_case__        =      {}
						snake_case__        =      """gelu"""



						def __init__(self:					Union[str, Any] ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Union[str, Any]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					List[Any]=False ,					__UpperCAmelCase:					Tuple=99 ,					__UpperCAmelCase:					Optional[int]=32 ,					__UpperCAmelCase:					str=5 ,					__UpperCAmelCase:					Optional[Any]=4 ,					__UpperCAmelCase:					str=37 ,					__UpperCAmelCase:					str=0.1 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=20 ,					__UpperCAmelCase:					Any=2 ,					__UpperCAmelCase:					List[Any]=1 ,					__UpperCAmelCase:					Union[str, Any]=0 ,					)    ->     Any:




											'''simple docstring'''



											__a					:						Tuple      =					parent
											__a					:						Optional[int]      =					batch_size
											__a					:						str      =					seq_length
											__a					:						Optional[Any]      =					is_training
											__a					:						List[str]      =					use_labels
											__a					:						List[str]      =					vocab_size
											__a					:						Optional[int]      =					hidden_size
											__a					:						List[str]      =					num_hidden_layers
											__a					:						Tuple      =					num_attention_heads
											__a					:						int      =					intermediate_size

											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						Union[str, Any]      =					attention_probs_dropout_prob
											__a					:						List[str]      =					max_position_embeddings
											__a					:						str      =					eos_token_id
											__a					:						Any      =					pad_token_id
											__a					:						Optional[Any]      =					bos_token_id



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     int:




											'''simple docstring'''



											__a					:						str      =					ids_tensor([self.batch_size, self.seq_length - 1] ,					self.vocab_size   ).clip(3 ,					self.vocab_size   )
											__a					:						Dict      =					np.expand_dims(np.array([self.eos_token_id] * self.batch_size   ) ,					1   )
											__a					:						Optional[Any]      =					np.concatenate([input_ids, eos_tensor] ,					axis=1   )

											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						List[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 ,					)
											__a					:						int      =					prepare_pegasus_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					List[Any]   )    ->     int:




											'''simple docstring'''



											__a					:						List[Any]      =					20
											__a					:						List[Any]      =					model_class_name(__UpperCAmelCase   )

											__a					:						Tuple      =					model.encode(inputs_dict["input_ids"]   )

											__a      ,			__a					:						Optional[Any]      =					(
											    inputs_dict["decoder_input_ids"],
											    inputs_dict["decoder_attention_mask"],
											)

											__a					:						Union[str, Any]      =					model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,					dtype="i4"   )

											__a					:						int      =					jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)
											__a					:						Any      =					model.decode(
											    decoder_input_ids[:, :-1] ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					past_key_values=__UpperCAmelCase ,					decoder_position_ids=__UpperCAmelCase ,					)

											__a					:						Dict      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,					dtype="i4"   )
											__a					:						Tuple      =					model.decode(
											    decoder_input_ids[:, -1:] ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					past_key_values=outputs_cache.past_key_values ,					decoder_position_ids=__UpperCAmelCase ,					)

											__a					:						Optional[int]      =					model.decode(__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						List[str]      =					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        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Tuple   )    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					20
											__a					:						Tuple      =					model_class_name(__UpperCAmelCase   )

											__a					:						Optional[int]      =					model.encode(inputs_dict["input_ids"]   )

											__a      ,			__a					:						Any      =					(
											    inputs_dict["decoder_input_ids"],
											    inputs_dict["decoder_attention_mask"],
											)

											__a					:						Union[str, Any]      =					jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
											    ] ,					axis=-1 ,					)

											__a					:						Any      =					model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Tuple      =					jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)

											__a					:						Tuple      =					model.decode(
											    decoder_input_ids[:, :-1] ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					past_key_values=__UpperCAmelCase ,					decoder_position_ids=__UpperCAmelCase ,					)
											__a					:						Union[str, Any]      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,					dtype="i4"   )
											__a					:						Optional[int]      =					model.decode(
											    decoder_input_ids[:, -1:] ,					__UpperCAmelCase ,					past_key_values=outputs_cache.past_key_values ,					decoder_attention_mask=__UpperCAmelCase ,					decoder_position_ids=__UpperCAmelCase ,					)

											__a					:						str      =					model.decode(__UpperCAmelCase ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase   )

											__a					:						Any      =					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_   (__A				,       __A				,       __A				,       __A=None				,       __A=None				,       )				->						List[str]:



					"""simple docstring"""
					if attention_mask is None:
										__a					:						str      =					np.not_equal(__A				,       config.pad_token_id      ).astype(np.inta      )
					if decoder_attention_mask is None:
										__a					:						Any      =					np.concatenate(
										    [
										        np.ones(decoder_input_ids[:, :1].shape				,       dtype=np.inta      ),
										        np.not_equal(decoder_input_ids[:, 1:]				,       config.pad_token_id      ).astype(np.inta      ),
										    ]				,       axis=-1				,       )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					}




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

						"""simple docstring"""



						snake_case__        =      (
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						snake_case__        =      (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						snake_case__        =      True
						snake_case__        =      False
						snake_case__        =      False
						snake_case__        =      False



						def        UpperCAmelCase__					(self:					int   )    ->     Optional[Any]:




											'''simple docstring'''



											__a					:						Any      =					FlaxPegasusModelTester(self   )
											__a					:						Union[str, Any]      =					ConfigTester(self ,					config_class=__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Union[str, Any]:




											'''simple docstring'''



											self.config_tester.run_common_tests()



						def        UpperCAmelCase__					(self:					Dict   )    ->     Optional[int]:




											'''simple docstring'''



											__a      ,			__a					:						Tuple      =					self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     str:




											'''simple docstring'''



											__a      ,			__a					:						Optional[int]      =					self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     List[Any]:




											'''simple docstring'''



											__a      ,			__a					:						List[str]      =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																					__a					:						Union[str, Any]      =					self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase   )
																					__a					:						Tuple      =					model_class(__UpperCAmelCase   )

																					@jax.jit
																					def encode_jitted(__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Tuple=None ,					**__UpperCAmelCase:					List[Any]   ):
																										return model.encode(input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase   )

																					with self.subTest("JIT Enabled"   ):
																										__a					:						Any      =					encode_jitted(**__UpperCAmelCase   ).to_tuple()

																					with self.subTest("JIT Disabled"   ):
																										with jax.disable_jit():
																															__a					:						str      =					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        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a      ,			__a					:						Union[str, Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																					__a					:						Tuple      =					model_class(__UpperCAmelCase   )
																					__a					:						Optional[Any]      =					model.encode(inputs_dict["input_ids"] ,					inputs_dict["attention_mask"]   )

																					__a					:						Dict      =					{
																					    "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:					int ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					List[str]   ):
																										return model.decode(
																										    decoder_input_ids=__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					encoder_outputs=__UpperCAmelCase ,					)

																					with self.subTest("JIT Enabled"   ):
																										__a					:						Any      =					decode_jitted(**__UpperCAmelCase   ).to_tuple()

																					with self.subTest("JIT Disabled"   ):
																										with jax.disable_jit():
																															__a					:						Optional[Any]      =					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        UpperCAmelCase__					(self:					Dict   )    ->     Tuple:




											'''simple docstring'''



											for model_class_name in self.all_model_classes:
																__a					:						Union[str, Any]      =					model_class_name.from_pretrained("google/pegasus-large" ,					from_pt=__UpperCAmelCase   )
																__a					:						Optional[int]      =					np.ones((1, 1)   )
																__a					:						Optional[int]      =					model(__UpperCAmelCase   )
																self.assertIsNotNone(__UpperCAmelCase   )



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						List[str]      =					FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"   )
											__a					:						Tuple      =					PegasusTokenizer.from_pretrained("google/pegasus-xsum"   )

											__a					:						Tuple      =					[
											    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
											    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
											]

											__a					:						List[Any]      =					[
											    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
											    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
											]

											__a					:						Any      =					tokenizer(__UpperCAmelCase ,					return_tensors="np" ,					truncation=__UpperCAmelCase ,					max_length=512 ,					padding=__UpperCAmelCase   )
											__a					:						List[str]      =					model.generate(**__UpperCAmelCase ,					num_beams=2   ).sequences
											__a					:						str      =					tokenizer.batch_decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase   )
											assert tgt_text == decoded





