



import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin


class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						@register_to_config
						def __init__(  self    :				int       ,	snake_case    :				int = 128       ,	snake_case    :				int = 256       ,	snake_case    :				float = 2000.0       ,	snake_case    :				int = 768       ,	snake_case    :				int = 12       ,	snake_case    :				int = 12       ,	snake_case    :				int = 64       ,	snake_case    :				int = 2048       ,	snake_case    :				float = 0.1       ,	):



											'''simple docstring'''


											super().__init__()

											SCREAMING_SNAKE_CASE							:     int						=				nn.Sequential(
											    nn.Linear(snake_case       ,	d_model * 4       ,	bias=snake_case	)       ,	nn.SiLU()       ,	nn.Linear(d_model * 4       ,	d_model * 4       ,	bias=snake_case	)       ,	nn.SiLU()       ,	)

											SCREAMING_SNAKE_CASE							:     Any						=				nn.Embedding(snake_case       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				False

											SCREAMING_SNAKE_CASE							:     Any						=				nn.Linear(snake_case       ,	snake_case       ,	bias=snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				nn.Dropout(p=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nn.ModuleList()
											for lyr_num in range(snake_case	):
																# FiLM conditional T5 decoder
																SCREAMING_SNAKE_CASE							:     int						=				DecoderLayer(d_model=snake_case       ,	d_kv=snake_case       ,	num_heads=snake_case       ,	d_ff=snake_case       ,	dropout_rate=snake_case	)
																self.decoders.append(snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				TaLayerNorm(snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				nn.Dropout(p=snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				nn.Linear(snake_case       ,	snake_case       ,	bias=snake_case	)







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				int       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				torch.mul(query_input.unsqueeze(-1	)       ,	key_input.unsqueeze(-2	)	)
											return mask.unsqueeze(-3	)







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Any       ,	snake_case    :				Tuple       ,	snake_case    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

											# decoder_noise_time is in [0, 1), so rescale to expected timing range.
											SCREAMING_SNAKE_CASE							:     Any						=				get_timestep_embedding(
											    decoder_noise_time * self.config.max_decoder_noise_time       ,	embedding_dim=self.config.d_model       ,	max_period=self.config.max_decoder_noise_time       ,	).to(dtype=self.dtype	)

											SCREAMING_SNAKE_CASE							:     str						=				self.conditioning_emb(snake_case	).unsqueeze(1	)

											assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				decoder_input_tokens.shape[1]

											# If we want to use relative positions for audio context, we can just offset
											# this sequence by the length of encodings_and_masks.
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.broadcast_to(
											    torch.arange(snake_case       ,	device=decoder_input_tokens.device	)       ,	(batch, seq_length)       ,	)

											SCREAMING_SNAKE_CASE							:     int						=				self.position_encoding(snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				self.continuous_inputs_projection(snake_case	)
											inputs += position_encodings
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.dropout(snake_case	)

											# decoder: No padding present.
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.ones(
											    decoder_input_tokens.shape[:2]       ,	device=decoder_input_tokens.device       ,	dtype=inputs.dtype	)

											# Translate encoding masks to encoder-decoder masks.
											SCREAMING_SNAKE_CASE							:     Dict						=				[(x, self.encoder_decoder_mask(snake_case       ,	snake_case	)) for x, y in encodings_and_masks]

											# cross attend style: concat encodings
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.cat([x[0] for x in encodings_and_encdec_masks]       ,	dim=1	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.cat([x[1] for x in encodings_and_encdec_masks]       ,	dim=-1	)

											for lyr in self.decoders:
																SCREAMING_SNAKE_CASE							:     int						=				lyr(
																    snake_case       ,	conditioning_emb=snake_case       ,	encoder_hidden_states=snake_case       ,	encoder_attention_mask=snake_case       ,	)[0]

											SCREAMING_SNAKE_CASE							:     Dict						=				self.decoder_norm(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.post_dropout(snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				self.spec_out(snake_case	)
											return spec_out


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Tuple       ,	snake_case    :				str       ,	snake_case    :				int       ,	snake_case    :				Optional[int]       ,	snake_case    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				Optional[int]=1E-6	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     str						=				nn.ModuleList()

											# cond self attention: layer 0
											self.layer.append(
											    TaLayerSelfAttentionCond(d_model=snake_case       ,	d_kv=snake_case       ,	num_heads=snake_case       ,	dropout_rate=snake_case	)	)

											# cross attention: layer 1
											self.layer.append(
											    TaLayerCrossAttention(
											        d_model=snake_case       ,	d_kv=snake_case       ,	num_heads=snake_case       ,	dropout_rate=snake_case       ,	layer_norm_epsilon=snake_case       ,	)	)

											# Film Cond MLP + dropout: last layer
											self.layer.append(
											    TaLayerFFCond(d_model=snake_case       ,	d_ff=snake_case       ,	dropout_rate=snake_case       ,	layer_norm_epsilon=snake_case	)	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				List[str]       ,	snake_case    :				Union[str, Any]=None       ,	snake_case    :				Any=None       ,	snake_case    :				Union[str, Any]=None       ,	snake_case    :				List[Any]=None       ,	snake_case    :				List[str]=None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.layer[0](
											    snake_case       ,	conditioning_emb=snake_case       ,	attention_mask=snake_case       ,	)

											if encoder_hidden_states is not None:
																SCREAMING_SNAKE_CASE							:     Dict						=				torch.where(encoder_attention_mask > 0       ,	0       ,	-1E10	).to(
																    encoder_hidden_states.dtype	)

																SCREAMING_SNAKE_CASE							:     Any						=				self.layer[1](
																    snake_case       ,	key_value_states=snake_case       ,	attention_mask=snake_case       ,	)

											# Apply Film Conditional Feed Forward layer
											SCREAMING_SNAKE_CASE							:     Dict						=				self.layer[-1](snake_case       ,	snake_case	)

											return (hidden_states,)


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Union[str, Any]       ,	snake_case    :				str       ,	snake_case    :				Dict       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				List[str]	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				TaLayerNorm(snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				TaFiLMLayer(in_features=d_model * 4       ,	out_features=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				Attention(query_dim=snake_case       ,	heads=snake_case       ,	dim_head=snake_case       ,	out_bias=snake_case       ,	scale_qk=snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nn.Dropout(snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[Any]       ,	snake_case    :				Any       ,	snake_case    :				Tuple=None       ,	snake_case    :				Union[str, Any]=None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.layer_norm(snake_case	)

											if conditioning_emb is not None:
																SCREAMING_SNAKE_CASE							:     Dict						=				self.FiLMLayer(snake_case       ,	snake_case	)

											# Self-attention block
											SCREAMING_SNAKE_CASE							:     str						=				self.attention(snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				hidden_states + self.dropout(snake_case	)

											return hidden_states


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				List[str]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Tuple       ,	snake_case    :				str       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     int						=				Attention(query_dim=snake_case       ,	heads=snake_case       ,	dim_head=snake_case       ,	out_bias=snake_case       ,	scale_qk=snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				TaLayerNorm(snake_case       ,	eps=snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nn.Dropout(snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[Any]       ,	snake_case    :				Dict       ,	snake_case    :				Optional[Any]=None       ,	snake_case    :				Union[str, Any]=None       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.layer_norm(snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				self.attention(
											    snake_case       ,	encoder_hidden_states=snake_case       ,	attention_mask=attention_mask.squeeze(1	)       ,	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				hidden_states + self.dropout(snake_case	)
											return layer_output


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Tuple       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Dict						=				TaDenseGatedActDense(d_model=snake_case       ,	d_ff=snake_case       ,	dropout_rate=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				TaFiLMLayer(in_features=d_model * 4       ,	out_features=snake_case	)
											SCREAMING_SNAKE_CASE							:     str						=				TaLayerNorm(snake_case       ,	eps=snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nn.Dropout(snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				Any       ,	snake_case    :				Optional[Any]=None	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.layer_norm(snake_case	)
											if conditioning_emb is not None:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.film(snake_case       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				self.DenseReluDense(snake_case	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				hidden_states + self.dropout(snake_case	)
											return hidden_states


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Any       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     List[Any]						=				nn.Linear(snake_case       ,	snake_case       ,	bias=snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				nn.Linear(snake_case       ,	snake_case       ,	bias=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.Linear(snake_case       ,	snake_case       ,	bias=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Dropout(snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				NewGELUActivation()







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.act(self.wi_a(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				self.wi_a(snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				hidden_gelu * hidden_linear
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.dropout(snake_case	)

											SCREAMING_SNAKE_CASE							:     int						=				self.wo(snake_case	)
											return hidden_states


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Union[str, Any]       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Any=1E-6	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				nn.Parameter(torch.ones(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				eps







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				hidden_states.to(torch.floataa	).pow(2	).mean(-1       ,	keepdim=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				hidden_states * torch.rsqrt(variance + self.variance_epsilon	)

											# convert into half-precision if necessary
											if self.weight.dtype in [torch.floataa, torch.bfloataa]:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				hidden_states.to(self.weight.dtype	)

											return self.weight * hidden_states


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Optional[Any]       ,	snake_case    :				torch.Tensor	):



											'''simple docstring'''


											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(snake_case       ,	3.0	))	))


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				List[Any]       ,	snake_case    :				str       ,	snake_case    :				List[str]	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     int						=				nn.Linear(snake_case       ,	out_features * 2       ,	bias=snake_case	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.scale_bias(snake_case	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.chunk(snake_case       ,	2       ,	-1	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				x * (1 + scale) + shift
											return x




import requests

_lowerCamelCase    :		int		      =    """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def        __a			(   __lowerCAmelCase   )     ->     None:
					# fetching a list of articles in json format
					SCREAMING_SNAKE_CASE							:     List[str]						=				requests.get(_NEWS_API + bbc_news_api_key   ).json()
					# each article in the list is a dict
					for i, article in enumerate(bbc_news_page['articles']      ,	1   ):
										print(F'''{i}.) {article['title']}'''   )


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")