


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a      )	->   Union[str, Any]:
      for param, grad_param in zip(model_a.parameters()			,				model_b.parameters()      ):
            if not param.requires_grad:
                  continue
            if not did_step:
                  # Grads should not be in sync
                  assert (
                      torch.allclose(param.grad			,				grad_param.grad      ) is False
                  ), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
            else:
                  # Grads should be in sync
                  assert (
                      torch.allclose(param.grad			,				grad_param.grad      ) is True
                  ), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a=True      )	->   str:
      model.train()
      a__	:  str     =   model(__a      )
      a__	:  Dict     =   F.mse_loss(__a			,				target.to(output.device      )      )
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(__a      )



def UpperCamelCase_				(				__a			,				__a=False      )	->   str:
      set_seed(42      )
      a__	:  int     =   RegressionModel()
      a__	:  Optional[int]     =   deepcopy(__a      )
      a__	:  List[str]     =   RegressionDataset(length=80      )
      a__	:  Tuple     =   DataLoader(__a			,				batch_size=16      )
      model.to(accelerator.device      )
      if sched:
            a__	:  str     =   AdamW(params=model.parameters()			,				lr=1e-3      )
            a__	:  Union[str, Any]     =   AdamW(params=ddp_model.parameters()			,				lr=1e-3      )
            a__	:  List[str]     =   LambdaLR(__a			,				lr_lambda=lambda __a      : epoch**0.65      )
            a__	:  Optional[Any]     =   LambdaLR(__a			,				lr_lambda=lambda __a      : epoch**0.65      )
      # Make a copy of `model`
      if sched:
            a__,	a__,	a__,	a__	:  Optional[Any]     =   accelerator.prepare(__a			,				__a			,				__a			,				__a      )
      else:
            a__,	a__	:  Optional[Any]     =   accelerator.prepare(__a			,				__a      )
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader



def UpperCamelCase_				(				__a      )	->   Tuple:
      # Test when on a single CPU or GPU that the context manager does nothing
      a__,	a__,	a__	:  Dict     =   get_training_setup(__a      )
      # Use a single batch
      a__,	a__	:  Optional[Any]     =   next(iter(__a      )      ).values()
      for iteration in range(3      ):
            # Gather the distributed inputs and targs for the base model
            a__,	a__	:  str     =   accelerator.gather((ddp_input, ddp_target)      )
            a__,	a__	:  Tuple     =   input.to(accelerator.device      ), target.to(accelerator.device      )
            # Perform our initial ground truth step in non "DDP"
            step_model(__a			,				__a			,				__a			,				__a      )
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(__a      ):
                        step_model(__a			,				__a			,				__a			,				__a      )
            else:
                  # Sync grads
                  step_model(__a			,				__a			,				__a			,				__a      )

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(__a			,				__a			,				__a			,				__a      )
            for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()      ):
                  if not param.requires_grad:
                        continue
                  assert torch.allclose(
                      param.grad			,				ddp_param.grad      ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

            # Shuffle ddp_input on each iteration
            torch.manual_seed(1_337 + iteration      )
            a__	:  Any     =   ddp_input[torch.randperm(len(__a      )      )]



def UpperCamelCase_				(				__a      )	->   Optional[Any]:
      # Test on distributed setup that context manager behaves properly
      a__,	a__,	a__	:  List[str]     =   get_training_setup(__a      )
      # Use a single batch
      a__,	a__	:  Optional[int]     =   next(iter(__a      )      ).values()
      for iteration in range(3      ):
            # Gather the distributed inputs and targs for the base model
            a__,	a__	:  Union[str, Any]     =   accelerator.gather((ddp_input, ddp_target)      )
            a__,	a__	:  Tuple     =   input.to(accelerator.device      ), target.to(accelerator.device      )
            # Perform our initial ground truth step in non "DDP"
            step_model(__a			,				__a			,				__a			,				__a      )
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(__a      ):
                        step_model(__a			,				__a			,				__a			,				__a      )
            else:
                  # Sync grads
                  step_model(__a			,				__a			,				__a			,				__a      )

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()      ):
                  if not param.requires_grad:
                        continue
                  if iteration % 2 == 0:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad			,				ddp_param.grad      ) is False
                        ), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad			,				ddp_param.grad      ) is True
                        ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1_337 + iteration      )
            a__	:  Dict     =   ddp_input[torch.randperm(len(__a      )      )]



def UpperCamelCase_				(				__a=False			,				__a=False      )	->   Optional[Any]:
      a__	:  List[str]     =   Accelerator(
          split_batches=__a			,				dispatch_batches=__a			,				gradient_accumulation_steps=2      )
      # Test that context manager behaves properly
      a__,	a__,	a__	:  Optional[int]     =   get_training_setup(__a      )
      for iteration, batch in enumerate(__a      ):
            a__,	a__	:  Optional[Any]     =   batch.values()
            # Gather the distributed inputs and targs for the base model
            a__,	a__	:  List[Any]     =   accelerator.gather((ddp_input, ddp_target)      )
            a__,	a__	:  Optional[int]     =   input.to(accelerator.device      ), target.to(accelerator.device      )
            # Perform our initial ground truth step in non "DDP"
            step_model(__a			,				__a			,				__a			,				__a			,				__a      )
            # Do "gradient accumulation" (noop)
            with accelerator.accumulate(__a      ):
                  step_model(__a			,				__a			,				__a			,				__a      )

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()      ):
                  if not param.requires_grad:
                        continue
                  if ((iteration + 1) % 2 == 0) or (iteration == len(__a      ) - 1):
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad			,				ddp_param.grad      ) is True
                        ), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad			,				ddp_param.grad      ) is False
                        ), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1_337 + iteration      )
            a__	:  Union[str, Any]     =   ddp_input[torch.randperm(len(__a      )      )]
      GradientState._reset_state()



def UpperCamelCase_				(				__a=False			,				__a=False      )	->   int:
      a__	:  List[Any]     =   Accelerator(
          split_batches=__a			,				dispatch_batches=__a			,				gradient_accumulation_steps=2      )
      # Test that context manager behaves properly
      a__,	a__,	a__,	a__,	a__,	a__,	a__	:  Any     =   get_training_setup(__a			,				__a      )
      for iteration, batch in enumerate(__a      ):
            a__,	a__	:  Tuple     =   batch.values()
            # Gather the distributed inputs and targs for the base model
            a__,	a__	:  List[str]     =   accelerator.gather((ddp_input, ddp_target)      )
            a__,	a__	:  int     =   input.to(accelerator.device      ), target.to(accelerator.device      )
            # Perform our initial ground truth step in non "DDP"
            model.train()
            ddp_model.train()
            step_model(__a			,				__a			,				__a			,				__a			,				__a      )
            opt.step()

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__a      )):
                  if split_batches:
                        sched.step()
                  else:
                        for _ in range(accelerator.num_processes      ):
                              sched.step()
            opt.zero_grad()
            # Perform gradient accumulation under wrapper
            with accelerator.accumulate(__a      ):
                  step_model(__a			,				__a			,				__a			,				__a      )
                  ddp_opt.step()
                  ddp_sched.step()
                  ddp_opt.zero_grad()

            # Learning rates should be the same
            assert (
                opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
            ), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
            a__	:  Union[str, Any]     =   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__a      ))
            if accelerator.num_processes > 1:
                  check_model_parameters(__a			,				__a			,				__a			,				__a      )
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1_337 + iteration      )
      GradientState._reset_state()



def UpperCamelCase_				(				)	->   List[str]:
      a__	:  Dict     =   Accelerator()

      a__	:  Dict     =   RegressionDataset(length=80      )
      a__	:  Optional[Any]     =   DataLoader(__a			,				batch_size=16      )
      a__	:  Optional[int]     =   RegressionDataset(length=96      )
      a__	:  Dict     =   DataLoader(__a			,				batch_size=16      )
      a__,	a__	:  Optional[int]     =   accelerator.prepare(__a			,				__a      )
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(__a      ):
            assert id(accelerator.gradient_state.active_dataloader      ) == id(__a      )
            if iteration < len(__a      ) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(__a      ):
                              assert id(accelerator.gradient_state.active_dataloader      ) == id(__a      )
                              if batch_num < len(__a      ) - 1:
                                    assert not accelerator.gradient_state.end_of_dataloader
                              else:
                                    assert accelerator.gradient_state.end_of_dataloader
            else:
                  assert accelerator.gradient_state.end_of_dataloader
      assert accelerator.gradient_state.active_dataloader is None



def UpperCamelCase_				(				)	->   List[Any]:
      a__	:  Optional[Any]     =   Accelerator()
      a__	:  Optional[Any]     =   accelerator.state
      if state.local_process_index == 0:
            print("**Test `accumulate` gradient accumulation with dataloader break**"      )
      test_dataloader_break()
      if state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print("**Test NOOP `no_sync` context manager**"      )
            test_noop_sync(__a      )
      if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
            if state.local_process_index == 0:
                  print("**Test Distributed `no_sync` context manager**"      )
            test_distributed_sync(__a      )
      if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                  for dispatch_batches in [True, False]:
                        if state.local_process_index == 0:
                              print(
                                  "**Test `accumulate` gradient accumulation, "			,				f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''			,				)
                        test_gradient_accumulation(__a			,				__a      )

    # Currently will break on torch 2.0 +, need to investigate why
      if is_torch_version("<"			,				"2.0"      ) or state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print(
                      "**Test `accumulate` gradient accumulation with optimizer and scheduler, "			,				"`split_batches=False`, `dispatch_batches=False`**"			,				)
            test_gradient_accumulation_with_opt_and_scheduler()
            if state.distributed_type == DistributedType.MULTI_GPU:
                  for split_batch in [True, False]:
                        for dispatch_batches in [True, False]:
                              if not split_batch and not dispatch_batches:
                                    continue
                              if state.local_process_index == 0:
                                    print(
                                        "**Test `accumulate` gradient accumulation with optimizer and scheduler, "			,				f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''			,				)
                              test_gradient_accumulation_with_opt_and_scheduler(__a			,				__a      )



def UpperCamelCase_				(				__a      )	->   List[str]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()





# 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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""microsoft/speecht5_tts"""
							_UpperCAmelCase      =						(
							    """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."""
							)
							_UpperCAmelCase      =						"""text_reader"""
							_UpperCAmelCase      =						SpeechTaProcessor
							_UpperCAmelCase      =						SpeechTaForTextToSpeech
							_UpperCAmelCase      =						SpeechTaHifiGan

							_UpperCAmelCase      =						["""text"""]
							_UpperCAmelCase      =						["""audio"""]






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if self.post_processor is None:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					'microsoft/speecht5_hifigan'
										super().setup()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					self.pre_processor(text=lowerCAmelCase__			,						return_tensors='pt'			,						truncation=lowerCAmelCase__       )

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

													SCREAMING_SNAKE_CASE_		:     List[Any]					=					load_dataset('Matthijs/cmu-arctic-xvectors'			,						split='validation'       )
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.tensor(embeddings_dataset[7_3_0_5]['xvector']       ).unsqueeze(0       )

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with torch.no_grad():
													return self.model.generate_speech(**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with torch.no_grad():
													return self.post_processor(lowerCAmelCase__       ).cpu().detach()





