
"""simple docstring"""





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_					:						int				= '''platform'''
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				PegasusConfig
			snake_case	      =				{}
			snake_case	      =				"gelu"




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







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						seq_length
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						vocab_size
								A_				 =						hidden_size
								A_				 =						num_hidden_layers
								A_				 =						num_attention_heads
								A_				 =						intermediate_size

								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						max_position_embeddings
								A_				 =						eos_token_id
								A_				 =						pad_token_id
								A_				 =						bos_token_id




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						ids_tensor([self.batch_size, self.seq_length - 1]       ,   self.vocab_size	).clip(3       ,   self.vocab_size	)
								A_				 =						np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)       ,   1	)
								A_				 =						np.concatenate([input_ids, eos_tensor]       ,   axis=1	)

								A_				 =						ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)

								A_				 =						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_				 =						prepare_pegasus_inputs_dict(_snake_case       ,   _snake_case       ,   _snake_case	)
								return config, inputs_dict




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					Dict       ,   _snake_case						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						20
								A_				 =						model_class_name(_snake_case	)

								A_				 =						model.encode(inputs_dict["input_ids"]	)

								A_ ,    A_				 =						(
								    inputs_dict["decoder_input_ids"],
								    inputs_dict["decoder_attention_mask"],
								)

								A_				 =						model.init_cache(decoder_input_ids.shape[0]       ,   _snake_case       ,   _snake_case	)
								A_				 =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,   dtype="i4"	)

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

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

								A_				 =						model.decode(_snake_case       ,   _snake_case	)

								A_				 =						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      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Dict	)      -> Tuple:







								"""simple docstring"""
								A_				 =						20
								A_				 =						model_class_name(_snake_case	)

								A_				 =						model.encode(inputs_dict["input_ids"]	)

								A_ ,    A_				 =						(
								    inputs_dict["decoder_input_ids"],
								    inputs_dict["decoder_attention_mask"],
								)

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

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

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

								A_				 =						model.decode(_snake_case       ,   _snake_case       ,   decoder_attention_mask=_snake_case	)

								A_				 =						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				,   ):

					'''simple docstring'''





					if attention_mask is None:
										A_				 =						np.not_equal(__a				,   config.pad_token_id				).astype(np.inta				)
					if decoder_attention_mask is None:
										A_				 =						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 __lowerCAmelCase				(					_lowercase     ,				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      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						FlaxPegasusModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case	)




			def      lowerCamelCase__     (				self						:					str	)      -> Any:







								"""simple docstring"""
								self.config_tester.run_common_tests()




			def      lowerCamelCase__     (				self						:					Tuple	)      -> str:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_snake_case       ,   _snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_ ,    A_				 =						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(_snake_case       ,   _snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__	):
																		A_				 =						self._prepare_for_class(_snake_case       ,   _snake_case	)
																		A_				 =						model_class(_snake_case	)

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

																		with self.subTest("JIT Enabled"	):
																							A_				 =						encode_jitted(**_snake_case	).to_tuple()

																		with self.subTest("JIT Disabled"	):
																							with jax.disable_jit():
																												A_				 =						encode_jitted(**_snake_case	).to_tuple()

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




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__	):
																		A_				 =						model_class(_snake_case	)
																		A_				 =						model.encode(inputs_dict["input_ids"]       ,   inputs_dict["attention_mask"]	)

																		A_				 =						{
																		    "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(_snake_case						:					List[Any]       ,   _snake_case						:					Any       ,   _snake_case						:					str	):
																							return model.decode(
																							    decoder_input_ids=_snake_case       ,   decoder_attention_mask=_snake_case       ,   encoder_outputs=_snake_case       ,   )

																		with self.subTest("JIT Enabled"	):
																							A_				 =						decode_jitted(**_snake_case	).to_tuple()

																		with self.subTest("JIT Disabled"	):
																							with jax.disable_jit():
																												A_				 =						decode_jitted(**_snake_case	).to_tuple()

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




			@slow
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> List[Any]:







								"""simple docstring"""
								for model_class_name in self.all_model_classes:
													A_				 =						model_class_name.from_pretrained("google/pegasus-large"       ,   from_pt=_snake_case	)
													A_				 =						np.ones((1, 1)	)
													A_				 =						model(_snake_case	)
													self.assertIsNotNone(_snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> int:







								"""simple docstring"""
								A_				 =						FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"	)
								A_				 =						PegasusTokenizer.from_pretrained("google/pegasus-xsum"	)

								A_				 =						[
								    " 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_				 =						[
								    "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_				 =						tokenizer(_snake_case       ,   return_tensors="np"       ,   truncation=_snake_case       ,   max_length=512       ,   padding=_snake_case	)
								A_				 =						model.generate(**_snake_case       ,   num_beams=2	).sequences
								A_				 =						tokenizer.batch_decode(_snake_case       ,   skip_special_tokens=_snake_case	)
								assert tgt_text == decoded






"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase_					:						int				= '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''



class __lowerCAmelCase				(					unittest.TestCase     ,				_lowercase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					str	)      -> Dict:







								"""simple docstring"""
								A_				 =						load_tool("text-question-answering"	)
								self.tool.setup()
								A_				 =						load_tool("text-question-answering"       ,   remote=_snake_case	)




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







								"""simple docstring"""
								A_				 =						self.tool(_snake_case       ,   "What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								A_				 =						self.remote_tool(_snake_case       ,   "What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.tool(text=_snake_case       ,   question="What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)






			def      lowerCamelCase__     (				self						:					str	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.remote_tool(text=_snake_case       ,   question="What did Hugging Face do in April 2021?"	)
								self.assertEqual(_snake_case       ,   "launched the BigScience Research Workshop"	)




