






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 		_snake_case  (  _snake_case					):
			_lowercase		:    torch.FloatTensor



class 		_snake_case  (  _snake_case   ,    _snake_case					):






			@register_to_config
			def __init__(       self		,					a = 6_5536		,					a = None		,					a = 2		,					a = 2		,					a = 0		,					a = "fourier"		,					a = True		,					a = False		,					a = 0.0		,					a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,					a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,					a = "UNetMidBlock1D"		,					a = None		,					a = (32, 32, 64)		,					a = None		,					a = 8		,					a = 1		,					a = False		,					)      ->       List[str]:
						super().__init__()
						SCREAMING_SNAKE_CASE						= sample_size

						# time
						if time_embedding_type == "fourier":
									SCREAMING_SNAKE_CASE						= GaussianFourierProjection(
									    embedding_size=8		,					set_W_to_weight=snake_case_		,					log=snake_case_		,					flip_sin_to_cos=snake_case_)
									SCREAMING_SNAKE_CASE						= 2 * block_out_channels[0]
						elif time_embedding_type == "positional":
									SCREAMING_SNAKE_CASE						= Timesteps(
									    block_out_channels[0]		,					flip_sin_to_cos=snake_case_		,					downscale_freq_shift=snake_case_)
									SCREAMING_SNAKE_CASE						= block_out_channels[0]

						if use_timestep_embedding:
									SCREAMING_SNAKE_CASE						= block_out_channels[0] * 4
									SCREAMING_SNAKE_CASE						= TimestepEmbedding(
									    in_channels=snake_case_		,					time_embed_dim=snake_case_		,					act_fn=snake_case_		,					out_dim=block_out_channels[0]		,					)

						SCREAMING_SNAKE_CASE						= nn.ModuleList([])
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= nn.ModuleList([])
						SCREAMING_SNAKE_CASE						= None

						# down
						SCREAMING_SNAKE_CASE						= in_channels
						for i, down_block_type in enumerate(snake_case_):
									SCREAMING_SNAKE_CASE						= output_channel
									SCREAMING_SNAKE_CASE						= block_out_channels[i]

									if i == 0:
												input_channel += extra_in_channels

									SCREAMING_SNAKE_CASE						= i == len(snake_case_) - 1

									SCREAMING_SNAKE_CASE						= get_down_block(
									    snake_case_		,					num_layers=snake_case_		,					in_channels=snake_case_		,					out_channels=snake_case_		,					temb_channels=block_out_channels[0]		,					add_downsample=not is_final_block or downsample_each_block		,					)
									self.down_blocks.append(snake_case_)

						# mid
						SCREAMING_SNAKE_CASE						= get_mid_block(
						    snake_case_		,					in_channels=block_out_channels[-1]		,					mid_channels=block_out_channels[-1]		,					out_channels=block_out_channels[-1]		,					embed_dim=block_out_channels[0]		,					num_layers=snake_case_		,					add_downsample=snake_case_		,					)

						# up
						SCREAMING_SNAKE_CASE						= list(reversed(snake_case_))
						SCREAMING_SNAKE_CASE						= reversed_block_out_channels[0]
						if out_block_type is None:
									SCREAMING_SNAKE_CASE						= out_channels
						else:
									SCREAMING_SNAKE_CASE						= block_out_channels[0]

						for i, up_block_type in enumerate(snake_case_):
									SCREAMING_SNAKE_CASE						= output_channel
									SCREAMING_SNAKE_CASE						= (
									    reversed_block_out_channels[i + 1] if i < len(snake_case_) - 1 else final_upsample_channels
									)

									SCREAMING_SNAKE_CASE						= i == len(snake_case_) - 1

									SCREAMING_SNAKE_CASE						= get_up_block(
									    snake_case_		,					num_layers=snake_case_		,					in_channels=snake_case_		,					out_channels=snake_case_		,					temb_channels=block_out_channels[0]		,					add_upsample=not is_final_block		,					)
									self.up_blocks.append(snake_case_)
									SCREAMING_SNAKE_CASE						= output_channel

						# out
						SCREAMING_SNAKE_CASE						= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,					32)
						SCREAMING_SNAKE_CASE						= get_out_block(
						    out_block_type=snake_case_		,					num_groups_out=snake_case_		,					embed_dim=block_out_channels[0]		,					out_channels=snake_case_		,					act_fn=snake_case_		,					fc_dim=block_out_channels[-1] // 4		,					)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = True		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= timestep
						if not torch.is_tensor(snake_case_):
									SCREAMING_SNAKE_CASE						= torch.tensor([timesteps]		,					dtype=torch.long		,					device=sample.device)
						elif torch.is_tensor(snake_case_) and len(timesteps.shape) == 0:
									SCREAMING_SNAKE_CASE						= timesteps[None].to(sample.device)

						SCREAMING_SNAKE_CASE						= self.time_proj(snake_case_)
						if self.config.use_timestep_embedding:
									SCREAMING_SNAKE_CASE						= self.time_mlp(snake_case_)
						else:
									SCREAMING_SNAKE_CASE						= timestep_embed[..., None]
									SCREAMING_SNAKE_CASE						= timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
									SCREAMING_SNAKE_CASE						= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

						# 2. down
						SCREAMING_SNAKE_CASE						= ()
						for downsample_block in self.down_blocks:
									SCREAMING_SNAKE_CASE						= downsample_block(hidden_states=snake_case_		,					temb=snake_case_)
									down_block_res_samples += res_samples

						# 3. mid
						if self.mid_block:
									SCREAMING_SNAKE_CASE						= self.mid_block(snake_case_		,					snake_case_)

						# 4. up
						for i, upsample_block in enumerate(self.up_blocks):
									SCREAMING_SNAKE_CASE						= down_block_res_samples[-1:]
									SCREAMING_SNAKE_CASE						= down_block_res_samples[:-1]
									SCREAMING_SNAKE_CASE						= upsample_block(snake_case_		,					res_hidden_states_tuple=snake_case_		,					temb=snake_case_)

						# 5. post-process
						if self.out_block:
									SCREAMING_SNAKE_CASE						= self.out_block(snake_case_		,					snake_case_)

						if not return_dict:
									return (sample,)

						return UNetaDOutput(sample=snake_case_)












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




