
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


UpperCamelCase_						=						os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
					from run_translation import main  # noqa


set_seed(42)
UpperCamelCase_						=						"sshleifer/student_marian_en_ro_6_1"
UpperCamelCase_						=						"sshleifer/tiny-mbart"

@require_torch
class      _SCREAMING_SNAKE_CASE				(		UpperCamelCase__				):





				def 							_UpperCAmelCase    (		self			:		Any       ,   snake_case_			:		int=False       ,   snake_case_			:		Any=None       ,   snake_case_			:		int=True       ,   snake_case_			:		Optional[int]=True       ,   snake_case_			:		Optional[Any]=True       ,   snake_case_			:		int=True       ,   ):


								"""simple docstring"""

								A  :	List[str]				=					self.run_trainer(
								    eval_steps=1       ,   max_len=12       ,   model_name=lowercase_       ,   num_train_epochs=1       ,   distributed=lowercase_       ,   extra_args_str=lowercase_       ,   predict_with_generate=lowercase_       ,   do_train=lowercase_       ,   do_eval=lowercase_       ,   do_predict=lowercase_       ,   )
								A  :	Tuple				=					TrainerState.load_from_json(os.path.join(lowercase_       ,   '''trainer_state.json'''				)				).log_history

								if not do_eval:
												return

								A  :	List[Any]				=					[log for log in logs if '''eval_loss''' in log.keys()]

								A  :	Optional[Any]				=					eval_metrics[0]
								if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												A  :	Optional[int]				=					eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']       ,   lowercase_				)
												assert not math.isnan(float(last_step_stats['''eval_loss''']				)				), "eval_loss must not be `nan`"





				@require_torch_non_multi_gpu
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								self.run_seqaseq_quick()





				@require_torch_multi_gpu
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_				)





				@require_torch_multi_gpu
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_				)





				@unittest.skip('''Requires an update of the env running those tests'''				)
				@require_torch_multi_gpu
				@require_fairscale
				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_       ,   extra_args_str='''--sharded_ddp simple'''				)





				@unittest.skip('''Requires an update of the env running those tests'''				)
				@require_torch_multi_gpu
				@require_fairscale
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_       ,   extra_args_str='''--sharded_ddp simple --fp16'''				)





				@unittest.skip('''Requires an update of the env running those tests'''				)
				@require_torch_multi_gpu
				@require_fairscale
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_       ,   extra_args_str='''--sharded_ddp zero_dp_2'''       ,   predict_with_generate=lowercase_				)





				@unittest.skip('''Requires an update of the env running those tests'''				)
				@require_torch_multi_gpu
				@require_fairscale
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								self.run_seqaseq_quick(
								    distributed=lowercase_       ,   extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''       ,   predict_with_generate=lowercase_				)





				@require_apex
				@require_torch_gpu
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								self.run_seqaseq_quick(distributed=lowercase_       ,   extra_args_str='''--fp16 --fp16_backend=apex'''				)
								# test 2nd time - was getting eval_loss': nan'
								# to reproduce the problem set distributed=False
								self.run_seqaseq_quick(distributed=lowercase_       ,   extra_args_str='''--fp16 --fp16_backend=apex'''				)





				@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']				)
				@require_torch_multi_gpu
				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	List[str]				=					{
								    # test with the default log_level - should be info and thus log info once
								    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
								    # test with low log_level and log_level_replica - should be noisy on all processes
								    # now the info string should appear twice on 2 processes
								    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
								    # test with high log_level and low log_level_replica
								    # now the info string should appear once only on the replica
								    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
								    # test with high log_level and log_level_replica - should be quiet on all processes
								    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
								}

								A  :	str				=					experiments[experiment_id]
								A  :	List[str]				=					{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
								A  :	str				=					'''Running training'''
								with CaptureStderr() as cl:
												self.run_seqaseq_quick(**lowercase_       ,   extra_args_str=data['''extra_args_str''']				)
								A  :	Optional[Any]				=					len(re.findall(lowercase_       ,   cl.err				)				)
								self.assertEqual(lowercase_       ,   data['''n_matches''']				)





				@slow
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	Optional[Any]				=					self.run_trainer(
								    eval_steps=2       ,   max_len=128       ,   model_name=lowercase_       ,   learning_rate=3E-4       ,   num_train_epochs=10       ,   distributed=lowercase_       ,   )

								# Check metrics
								A  :	int				=					TrainerState.load_from_json(os.path.join(lowercase_       ,   '''trainer_state.json'''				)				).log_history
								A  :	Any				=					[log for log in logs if '''eval_loss''' in log.keys()]
								A  :	Optional[Any]				=					eval_metrics[0]
								A  :	List[str]				=					eval_metrics[-1]

								assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
								assert isinstance(last_step_stats['''eval_bleu''']       ,   lowercase_				)

								# test if do_predict saves generations and metrics
								A  :	str				=					os.listdir(lowercase_				)
								A  :	str				=					{os.path.basename(lowercase_				) for p in contents}
								assert "generated_predictions.txt" in contents
								assert "predict_results.json" in contents





				@slow
				@require_bitsandbytes
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(snake_case_			:		str				) -> Tuple[int, float]:
												A  :	Optional[int]				=					'''--skip_memory_metrics 0'''

												A  :	Tuple				=					self.run_trainer(
												    max_len=128       ,   model_name=lowercase_       ,   learning_rate=3E-4       ,   num_train_epochs=1       ,   optim=lowercase_       ,   distributed=lowercase_       ,   extra_args_str=lowercase_       ,   do_eval=lowercase_       ,   do_predict=lowercase_       ,   n_gpus_to_use=1       ,   )

												# Check metrics
												A  :	str				=					TrainerState.load_from_json(Path(lowercase_       ,   '''trainer_state.json'''				)				).log_history
												A  :	Tuple				=					int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20				)
												A  :	Dict				=					int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20				)

												A  :	List[Any]				=					logs[0]['''train_loss''']
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

								A			,							A			,							A  :	int				=					train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
								A			,							A			,							A  :	Tuple				=					train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

								A  :	List[Any]				=					gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								A  :	Optional[int]				=					gpu_peak_mem_orig + gpu_alloc_mem_orig
								A  :	Dict				=					gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								A  :	Tuple				=					gpu_total_mem_orig - gpu_total_mem_bnb

								# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
								# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
								# in 2 bytes and the diff in optim memory usage is derived as so:
								#
								# - normal 25*8=~200MB (8 bytes per param)
								# - bnb    25*2= ~50MB (2 bytes per param)
								#
								# Thus we should expect ~150MB total memory saved.
								#
								# Peak memory should be the same - the total should be different by about that same margin
								#
								# After leaving a small margin to accommodate for differences between gpus let's check
								# that we have at least 120MB in savings
								A  :	str				=					120

								# uncomment the following if this test starts failing - requires py38 for a new print feature
								# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
								# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
								# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
								# print(f"{gpu_alloc_mem_diff=}MB")
								# print(f"{gpu_peak_mem_diff=}MB")
								# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
								# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

								self.assertGreater(
								    lowercase_       ,   lowercase_       ,   '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
								    f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
								    f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""       ,   )

								self.assertGreater(
								    lowercase_       ,   lowercase_       ,   '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
								    f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
								    f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""       ,   )

								self.assertEqual(
								    lowercase_       ,   lowercase_       ,   f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""				)






				def 							_UpperCAmelCase    (		self			:		Any       ,   snake_case_			:		int       ,   snake_case_			:		str       ,   snake_case_			:		int       ,   snake_case_			:		float = 3E-3       ,   snake_case_			:		str = "adafactor"       ,   snake_case_			:		bool = False       ,   snake_case_			:		str = None       ,   snake_case_			:		int = 0       ,   snake_case_			:		bool = True       ,   snake_case_			:		bool = True       ,   snake_case_			:		bool = True       ,   snake_case_			:		bool = True       ,   snake_case_			:		int = None       ,   ):


								"""simple docstring"""

								A  :	List[str]				=					self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								A  :	Tuple				=					self.get_auto_remove_tmp_dir()
								A  :	List[Any]				=					f"""\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(lowercase_				)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(lowercase_				)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

								A  :	Optional[Any]				=					f"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(lowercase_				)}\n        """.split()

								A  :	Tuple				=					'''\n            --do_predict\n        '''.split()

								A  :	str				=					[]
								if do_train:
												args += args_train

								if do_eval:
												args += args_eval

								if do_predict:
												args += args_predict

								if predict_with_generate:
												args += "--predict_with_generate".split()

								if do_train:
												if optim == "adafactor":
																args += "--adafactor".split()
												else:
																args += f"""--optim {optim}""".split()

								if extra_args_str is not None:
												args += extra_args_str.split()

								if distributed:
												if n_gpus_to_use is None:
																A  :	Union[str, Any]				=					get_gpu_count()
												A  :	str				=					get_torch_dist_unique_port()
												A  :	Optional[int]				=					f"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
												A  :	List[Any]				=					[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(lowercase_       ,   env=self.get_env()				)
								else:
												A  :	Dict				=					['''run_translation.py'''] + args
												with patch.object(lowercase_       ,   '''argv'''       ,   lowercase_				):
																main()

								return output_dir






'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

         if self.vqvae is not None:
             A__							=     self.vqvae.encode(torch.unsqueeze(lowercase_		,			0				)				).latent_dist.sample(
                 generator=lowercase_				)[0]
             A__							=     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			self.scheduler.timesteps[start_step - 1]				)

         A__							=     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         A__							=     int(mask_start_secs * pixels_per_second				)
         A__							=     int(mask_end_secs * pixels_per_second				)
         A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			torch.tensor(self.scheduler.timesteps[start_step:]				)				)

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
         if isinstance(self.unet		,			lowercase_				):
             A__							=     self.unet(lowercase_		,			lowercase_		,			lowercase_				)["sample"]
         else:
             A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["prev_sample"]

         if mask is not None:
             if mask_start > 0:
                 A__							=     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 A__							=     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         A__							=     1 / self.vqvae.config.scaling_factor * images
         A__							=     self.vqvae.decode(lowercase_				)["sample"]

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

     A__							=     [self.mel.image_to_audio(lowercase_				) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,			(0,)				)				):
         A__							=     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         A__							=     self.scheduler.alphas_cumprod[t]
         A__							=     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         A__							=     1 - alpha_prod_t
         A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]
         A__							=     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         A__							=     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         A__							=     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample



 @staticmethod
 def 	UpperCAmelCase_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



