

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= filter(lambda lowercase__	: p.requires_grad			, model.parameters()	)
	snake_case_      		= sum([np.prod(p.size()	) for p in model_parameters]	)
	return params


A											=     logging.getLogger(__name__)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if metric == "rouge2":
		snake_case_      		= '{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		snake_case_      		= '{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		snake_case_      		= '{val_avg_em:.4f}-{step_count}'
	elif metric == "loss":
		snake_case_      		= '{val_avg_loss:.4f}-{step_count}'
	else:
		raise NotImplementedError(
		    f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
		    ' function.'	)

	snake_case_      		= ModelCheckpoint(
	    dirpath=lowercase__			, filename=lowercase__			, monitor=f"""val_{metric}"""			, mode='max'			, save_top_k=1			, every_n_epochs=1			, )

	return checkpoint_callback





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	return EarlyStopping(
	    monitor=f"""val_{metric}"""			, mode='min' if 'loss' in metric else 'max'			, patience=lowercase__			, verbose=lowercase__			, )






class SCREAMING_SNAKE_CASE  (		pl.Callback							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
				pl_module.logger.log_metrics(__UpperCamelCase							)


			@rank_zero_only
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=True							):

				"""simple docstring"""


				logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
				snake_case_      		= trainer.callback_metrics
				trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
				# Log results
				snake_case_      		= Path(pl_module.hparams.output_dir							)
				if type_path == "test":
					snake_case_      		= od / 'test_results.txt'
					snake_case_      		= od / 'test_generations.txt'
				else:
					# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
					# If people want this it will be easy enough to add back.
					snake_case_      		= od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
					snake_case_      		= od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
					results_file.parent.mkdir(exist_ok=__UpperCamelCase							)
					generations_file.parent.mkdir(exist_ok=__UpperCamelCase							)
				with open(__UpperCamelCase ,				'a+'							) as writer:
					for key in sorted(__UpperCamelCase							):
						if key in ["log", "progress_bar", "preds"]:
							continue
						snake_case_      		= metrics[key]
						if isinstance(__UpperCamelCase ,				torch.Tensor							):
							snake_case_      		= val.item()
						snake_case_      		= f"""{key}: {val:.6f}\n"""
						writer.write(__UpperCamelCase							)

				if not save_generations:
					return

				if "preds" in metrics:
					snake_case_      		= '\n'.join(metrics['preds']							)
					generations_file.open('w+'							).write(__UpperCamelCase							)


			@rank_zero_only
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				try:
					snake_case_      		= pl_module.model.model.num_parameters()
				except AttributeError:
					snake_case_      		= pl_module.model.num_parameters()

				snake_case_      		= count_trainable_parameters(__UpperCamelCase							)
				# mp stands for million parameters
				trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}							)


			@rank_zero_only
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				save_json(pl_module.metrics ,				pl_module.metrics_save_path							)
				return self._write_logs(__UpperCamelCase ,				__UpperCamelCase ,				'test'							)







			@rank_zero_only
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				save_json(pl_module.metrics ,				pl_module.metrics_save_path							)
				# Uncommenting this will save val generations
				# return self._write_logs(trainer, pl_module, "valid")



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
