





'''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
					lowerCAmelCase       		=      """platform"""
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 		lowerCamelCase  :
	snake_case_      =   PegasusConfig
	snake_case_      =   {}
	snake_case_      =   "gelu"



	def __init__(   self		,      a_		,      a_=13		,      a_=7		,      a_=True		,      a_=False		,      a_=99		,      a_=32		,      a_=5		,      a_=4		,      a_=37		,      a_=0.1		,      a_=0.1		,      a_=20		,      a_=2		,      a_=1		,      a_=0		,      ):
			lowerCAmelCase     :    List[str]						=   parent
			lowerCAmelCase     :    int						=   batch_size
			lowerCAmelCase     :    Optional[Any]						=   seq_length
			lowerCAmelCase     :    str						=   is_training
			lowerCAmelCase     :    int						=   use_labels
			lowerCAmelCase     :    Dict						=   vocab_size
			lowerCAmelCase     :    Optional[int]						=   hidden_size
			lowerCAmelCase     :    List[Any]						=   num_hidden_layers
			lowerCAmelCase     :    int						=   num_attention_heads
			lowerCAmelCase     :    Any						=   intermediate_size

			lowerCAmelCase     :    int						=   hidden_dropout_prob
			lowerCAmelCase     :    Optional[int]						=   attention_probs_dropout_prob
			lowerCAmelCase     :    Tuple						=   max_position_embeddings
			lowerCAmelCase     :    str						=   eos_token_id
			lowerCAmelCase     :    Tuple						=   pad_token_id
			lowerCAmelCase     :    Any						=   bos_token_id



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size	).clip(3		,      self.vocab_size	)
			lowerCAmelCase     :    List[str]						=   np.expand_dims(np.array([self.eos_token_id] * self.batch_size	)		,      1	)
			lowerCAmelCase     :    Optional[int]						=   np.concatenate([input_ids, eos_tensor]		,      axis=1	)

			lowerCAmelCase     :    Optional[int]						=   ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size	)

			lowerCAmelCase     :    List[str]						=   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		,      )
			lowerCAmelCase     :    str						=   prepare_pegasus_inputs_dict(a_		,      a_		,      a_	)
			return config, inputs_dict



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   20
			lowerCAmelCase     :    Optional[int]						=   model_class_name(a_	)

			lowerCAmelCase     :    Dict						=   model.encode(inputs_dict["input_ids"]	)

			lowerCAmelCase     :    List[str]						=   (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase     :    List[Any]						=   model.init_cache(decoder_input_ids.shape[0]		,      a_		,      a_	)
			lowerCAmelCase     :    List[str]						=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,      dtype="i4"	)

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

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

			lowerCAmelCase     :    Dict						=   model.decode(a_		,      a_	)

			lowerCAmelCase     :    Union[str, 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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[int]						=   20
			lowerCAmelCase     :    Optional[Any]						=   model_class_name(a_	)

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

			lowerCAmelCase     :    List[str]						=   (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

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

			lowerCAmelCase     :    Union[str, Any]						=   model.init_cache(decoder_input_ids.shape[0]		,      a_		,      a_	)
			lowerCAmelCase     :    Tuple						=   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]		,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,      )

			lowerCAmelCase     :    Tuple						=   model.decode(
			    decoder_input_ids[:, :-1]		,      a_		,      decoder_attention_mask=a_		,      past_key_values=a_		,      decoder_position_ids=a_		,      )
			lowerCAmelCase     :    Any						=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,      dtype="i4"	)
			lowerCAmelCase     :    List[Any]						=   model.decode(
			    decoder_input_ids[:, -1:]		,      a_		,      past_key_values=outputs_cache.past_key_values		,      decoder_attention_mask=a_		,      decoder_position_ids=a_		,      )

			lowerCAmelCase     :    Tuple						=   model.decode(a_		,      a_		,      decoder_attention_mask=a_	)

			lowerCAmelCase     :    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      __A						(							a_						:  Optional[int]      ,a_						:  str      ,a_						:  str      ,a_						:  Optional[Any]=None      ,a_						:  Optional[int]=None      ,):
		if attention_mask is None:
				lowerCAmelCase     :    Tuple						=   np.not_equal(a_      ,config.pad_token_id	).astype(np.inta	)
		if decoder_attention_mask is None:
				lowerCAmelCase     :    Dict						=   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  (      _A      ,   unittest.TestCase       ):
	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	):
			lowerCAmelCase     :    List[str]						=   FlaxPegasusModelTester(self	)
			lowerCAmelCase     :    int						=   ConfigTester(self		,      config_class=a_	)



	def   _lowerCamelCase    (   self	):
			self.config_tester.run_common_tests()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a_		,      a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   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(a_		,      a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
							lowerCAmelCase     :    Optional[Any]						=   self._prepare_for_class(a_		,      a_	)
							lowerCAmelCase     :    Optional[Any]						=   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"	):
									lowerCAmelCase     :    int						=   encode_jitted(**a_	).to_tuple()

							with self.subTest("JIT Disabled"	):
									with jax.disable_jit():
											lowerCAmelCase     :    List[Any]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
							lowerCAmelCase     :    Optional[int]						=   model_class(a_	)
							lowerCAmelCase     :    int						=   model.encode(inputs_dict["input_ids"]		,      inputs_dict["attention_mask"]	)

							lowerCAmelCase     :    Optional[int]						=   {
							    "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"	):
									lowerCAmelCase     :    Tuple						=   decode_jitted(**a_	).to_tuple()

							with self.subTest("JIT Disabled"	):
									with jax.disable_jit():
											lowerCAmelCase     :    int						=   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   _lowerCamelCase    (   self	):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase     :    str						=   model_class_name.from_pretrained("google/pegasus-large"		,      from_pt=a_	)
					lowerCAmelCase     :    Tuple						=   np.ones((1, 1)	)
					lowerCAmelCase     :    Tuple						=   model(a_	)
					self.assertIsNotNone(a_	)





	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"	)
			lowerCAmelCase     :    int						=   PegasusTokenizer.from_pretrained("google/pegasus-xsum"	)

			lowerCAmelCase     :    Optional[Any]						=   [
			    " 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!\" ",
			]

			lowerCAmelCase     :    List[str]						=   [
			    "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.",
			]

			lowerCAmelCase     :    str						=   tokenizer(a_		,      return_tensors="np"		,      truncation=a_		,      max_length=512		,      padding=a_	)
			lowerCAmelCase     :    str						=   model.generate(**a_		,      num_beams=2	).sequences
			lowerCAmelCase     :    Tuple						=   tokenizer.batch_decode(a_		,      skip_special_tokens=a_	)
			assert tgt_text == decoded









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



