





'''simple docstring'''






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase       		=      """true"""






def      __A						(							a_						:  Optional[int]      ,a_						:  Dict=8_2      ,a_						:  int=1_6	):
		set_seed(4_2	)
		lowerCAmelCase     :    Optional[int]						=   RegressionModel()
		lowerCAmelCase     :    Any						=   deepcopy(a_	)
		lowerCAmelCase     :    Tuple						=   RegressionDataset(length=a_	)
		lowerCAmelCase     :    Any						=   DataLoader(a_      ,batch_size=a_	)
		model.to(accelerator.device	)
		lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   accelerator.prepare(a_      ,a_	)
		return model, ddp_model, dataloader






def      __A						(							a_						:  Accelerator      ,a_						:  Any=False	):
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
		lowerCAmelCase     :    Any						=   load_dataset("glue"      ,"mrpc"      ,split="validation"	)

		def tokenize_function(a_						:  Tuple	):
				lowerCAmelCase     :    int						=   tokenizer(examples["sentence1"]      ,examples["sentence2"]      ,truncation=a_      ,max_length=a_	)
				return outputs

		with accelerator.main_process_first():
				lowerCAmelCase     :    Optional[Any]						=   dataset.map(
				    a_      ,batched=a_      ,remove_columns=["idx", "sentence1", "sentence2"]      ,)

		lowerCAmelCase     :    Optional[int]						=   tokenized_datasets.rename_column("label"      ,"labels"	)

		def collate_fn(a_						:  List[Any]	):
				if use_longest:
						return tokenizer.pad(a_      ,padding="longest"      ,return_tensors="pt"	)
				return tokenizer.pad(a_      ,padding="max_length"      ,max_length=1_2_8      ,return_tensors="pt"	)

		return DataLoader(a_      ,shuffle=a_      ,collate_fn=a_      ,batch_size=1_6	)






def      __A						(							a_						:  Any      ,a_						:  Dict	):
		lowerCAmelCase     :    Tuple						=   Accelerator(dispatch_batches=a_      ,split_batches=a_	)
		lowerCAmelCase     :    str						=   get_dataloader(a_      ,not dispatch_batches	)
		lowerCAmelCase     :    Optional[int]						=   AutoModelForSequenceClassification.from_pretrained(
		    "hf-internal-testing/mrpc-bert-base-cased"      ,return_dict=a_	)
		lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   accelerator.prepare(a_      ,a_	)
		return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def      __A						(							a_						:  Optional[int]      ,a_						:  List[Any]      ,a_						:  Dict	):
		lowerCAmelCase     :    List[Any]						=   []
		for batch in dataloader:
				lowerCAmelCase	,		lowerCAmelCase     :    str						=   batch.values()
				with torch.no_grad():
						lowerCAmelCase     :    Optional[int]						=   model(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   accelerator.gather_for_metrics((logit, target)	)
						logits_and_targets.append((logit, target)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   [], []
		for logit, targ in logits_and_targets:
				logits.append(a_	)
				targs.append(a_	)
		lowerCAmelCase	,		lowerCAmelCase     :    List[Any]						=   torch.cat(a_	), torch.cat(a_	)
		return logits, targs






def      __A						(							a_						:  Accelerator      ,a_						:  Optional[Any]=8_2      ,a_						:  Dict=False      ,a_						:  Dict=False      ,a_						:  Optional[Any]=1_6	):
		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   get_basic_setup(a_      ,a_      ,a_	)
		lowerCAmelCase	,		lowerCAmelCase     :    Optional[int]						=   generate_predictions(a_      ,a_      ,a_	)
		assert (
		    len(a_	) == num_samples
		), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a_	)}'''






def      __A						(							a_						:  bool = False      ,a_						:  bool = False	):
		lowerCAmelCase     :    Any						=   evaluate.load("glue"      ,"mrpc"	)
		lowerCAmelCase	,		lowerCAmelCase     :    List[Any]						=   get_mrpc_setup(a_      ,a_	)
		# First do baseline
		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   setup["no"]
		model.to(a_	)
		model.eval()
		for batch in dataloader:
				batch.to(a_	)
				with torch.inference_mode():
						lowerCAmelCase     :    Union[str, Any]						=   model(**a_	)
				lowerCAmelCase     :    str						=   outputs.logits.argmax(dim=-1	)
				metric.add_batch(predictions=a_      ,references=batch["labels"]	)
		lowerCAmelCase     :    str						=   metric.compute()

		# Then do distributed
		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    int						=   setup["ddp"]
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						lowerCAmelCase     :    Union[str, Any]						=   model(**a_	)
				lowerCAmelCase     :    Optional[int]						=   outputs.logits.argmax(dim=-1	)
				lowerCAmelCase     :    Union[str, Any]						=   batch["labels"]
				lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   accelerator.gather_for_metrics((preds, references)	)
				metric.add_batch(predictions=a_      ,references=a_	)
		lowerCAmelCase     :    Dict						=   metric.compute()

		for key in "accuracy f1".split():
				assert math.isclose(
				    baseline[key]      ,distributed[key]	), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''






def      __A						(							):
		lowerCAmelCase     :    Any						=   Accelerator(split_batches=a_      ,dispatch_batches=a_	)
		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_warning()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		# These are a bit slower so they should only be ran on the GPU or TPU
		if torch.cuda.is_available() or is_tpu_available():
				if accelerator.is_local_main_process:
						print("**Testing gather_for_metrics**"	)
				for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
								if accelerator.is_local_main_process:
										print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''	)
								test_mrpc(a_      ,a_	)
								accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print("**Test torch metrics**"	)
		for split_batches in [True, False]:
				for dispatch_batches in [True, False]:
						lowerCAmelCase     :    Optional[Any]						=   Accelerator(split_batches=a_      ,dispatch_batches=a_	)
						if accelerator.is_local_main_process:
								print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''	)
						test_torch_metrics(a_      ,9_9	)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print("**Test last batch is not dropped when perfectly divisible**"	)
		lowerCAmelCase     :    Dict						=   Accelerator()
		test_torch_metrics(a_      ,5_1_2	)
		accelerator.state._reset_state()






def      __A						(							a_						:  Any	):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
					main()










'''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()



