

'''simple docstring'''


import functools
import operator

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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "unispeech"


							def __init__(			self    ,			_snake_case=32    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=3072    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case="group"    ,			_snake_case="gelu"    ,			_snake_case=(512, 512, 512, 512, 512, 512, 512)    ,			_snake_case=(5, 2, 2, 2, 2, 2, 2)    ,			_snake_case=(10, 3, 3, 3, 3, 2, 2)    ,			_snake_case=False    ,			_snake_case=128    ,			_snake_case=16    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=0.05    ,			_snake_case=10    ,			_snake_case=2    ,			_snake_case=0.0    ,			_snake_case=10    ,			_snake_case=0    ,			_snake_case=320    ,			_snake_case=2    ,			_snake_case=0.1    ,			_snake_case=100    ,			_snake_case=256    ,			_snake_case=256    ,			_snake_case=0.1    ,			_snake_case="mean"    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=256    ,			_snake_case=80    ,			_snake_case=0    ,			_snake_case=1    ,			_snake_case=2    ,			_snake_case=0.5    ,			**_snake_case    ,			)							->				Any:
								super().__init__(**_snake_case    ,			pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					feat_extract_norm
								UpperCAmelCase_		:      Union[str, Any]       	=					feat_extract_activation
								UpperCAmelCase_		:      Union[str, Any]       	=					list(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					list(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					list(_snake_case)
								UpperCAmelCase_		:      Dict       	=					conv_bias
								UpperCAmelCase_		:      str       	=					num_conv_pos_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					num_conv_pos_embedding_groups
								UpperCAmelCase_		:      Optional[int]       	=					len(self.conv_dim)
								UpperCAmelCase_		:      Optional[int]       	=					num_hidden_layers
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_act
								UpperCAmelCase_		:      int       	=					num_attention_heads
								UpperCAmelCase_		:      int       	=					hidden_dropout
								UpperCAmelCase_		:      Optional[int]       	=					attention_dropout
								UpperCAmelCase_		:      Dict       	=					activation_dropout
								UpperCAmelCase_		:      Dict       	=					feat_proj_dropout
								UpperCAmelCase_		:      str       	=					final_dropout
								UpperCAmelCase_		:      List[Any]       	=					layerdrop
								UpperCAmelCase_		:      Dict       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[Any]       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					num_ctc_classes
								UpperCAmelCase_		:      int       	=					vocab_size
								UpperCAmelCase_		:      List[str]       	=					do_stable_layer_norm
								UpperCAmelCase_		:      List[Any]       	=					use_weighted_layer_sum
								UpperCAmelCase_		:      Optional[Any]       	=					classifier_proj_size

								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
								UpperCAmelCase_		:      Optional[Any]       	=					apply_spec_augment
								UpperCAmelCase_		:      Optional[Any]       	=					mask_time_prob
								UpperCAmelCase_		:      str       	=					mask_time_length
								UpperCAmelCase_		:      int       	=					mask_time_min_masks
								UpperCAmelCase_		:      str       	=					mask_feature_prob
								UpperCAmelCase_		:      int       	=					mask_feature_length
								UpperCAmelCase_		:      int       	=					mask_feature_min_masks

								# parameters for pretraining with codevector quantized representations
								UpperCAmelCase_		:      Any       	=					num_codevectors_per_group
								UpperCAmelCase_		:      Any       	=					num_codevector_groups
								UpperCAmelCase_		:      Optional[int]       	=					contrastive_logits_temperature
								UpperCAmelCase_		:      Union[str, Any]       	=					feat_quantizer_dropout
								UpperCAmelCase_		:      Any       	=					num_negatives
								UpperCAmelCase_		:      List[Any]       	=					codevector_dim
								UpperCAmelCase_		:      List[Any]       	=					proj_codevector_dim
								UpperCAmelCase_		:      Optional[Any]       	=					diversity_loss_weight

								# ctc loss
								UpperCAmelCase_		:      Any       	=					ctc_loss_reduction
								UpperCAmelCase_		:      Tuple       	=					ctc_zero_infinity

								# pretraining loss
								UpperCAmelCase_		:      Dict       	=					replace_prob





							@property
							def 						_snake_case							(			self)							->				Any:
								return functools.reduce(operator.mul    ,			self.conv_stride    ,			1)









'''simple docstring'''


from __future__ import annotations

import requests

lowerCAmelCase__										= set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1   ,UpperCamelCase = "new"   ,UpperCamelCase = None  )    ->    dict:
	UpperCAmelCase_		:      Dict       	=					wanted_data or []
	if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase  ) - valid_terms  )  ):
		UpperCAmelCase_		:      str       	=					f"""Invalid search term: {invalid_search_terms}"""
		raise ValueError(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					requests.get(
	    f"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""   ,headers={'User-agent': 'A random string'}   ,)
	if response.status_code == 4_2_9:
		raise requests.HTTPError

	UpperCAmelCase_		:      int       	=					response.json()
	if not wanted_data:
		return {id_: data["data"]["children"][id_] for id_ in range(UpperCamelCase  )}

	UpperCAmelCase_		:      List[str]       	=					{}
	for id_ in range(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					{
		    item: data['data']['children'][id_]['data'][item] for item in wanted_data
		}
	return data_dict


if __name__ == "__main__":
						# If you get Error 429, that means you are rate limited.Try after some time
						print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))






