





from unittest.mock import Mock, patch

from file_transfer.send_file import send_file





@patch('socket.socket'  )
@patch('builtins.open'  )
def   lowerCamelCase__   (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			Mock()
					_snake_case													=			conn, Mock()
					_snake_case													=			iter([1, None]  )
					_snake_case													=			lambda UpperCamelCase__  : next(lowerCamelCase__  )

					# ===== invoke =====
					send_file(filename='mytext.txt'    ,      testing=lowerCamelCase__  )

					# ===== ensurance =====
					sock.assert_called_once()
					sock.return_value.bind.assert_called_once()
					sock.return_value.listen.assert_called_once()
					sock.return_value.accept.assert_called_once()
					conn.recv.assert_called_once()

					file.return_value.__enter__.assert_called_once()
					file.return_value.__enter__.return_value.read.assert_called()

					conn.send.assert_called_once()
					conn.close.assert_called_once()
					sock.return_value.shutdown.assert_called_once()
					sock.return_value.close.assert_called_once()










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      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

										_snake_case													=			nn.ModuleList()
										for lyr_num in range(lowerCAmelCase_					):
															# FiLM conditional T5 decoder
															_snake_case													=			DecoderLayer(d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
															self.decoders.append(lowerCAmelCase_					)

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_snake_case													=			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					)

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

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

										_snake_case													=			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.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

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

										# Translate encoding masks to encoder-decoder masks.
										_snake_case													=			[(x, self.encoder_decoder_mask(lowerCAmelCase_			,  lowerCAmelCase_					)) for x, y in encodings_and_masks]

										# cross attend style: concat encodings
										_snake_case													=			torch.cat([x[0] for x in encodings_and_encdec_masks]			,  dim=1					)
										_snake_case													=			torch.cat([x[1] for x in encodings_and_encdec_masks]			,  dim=-1					)

										for lyr in self.decoders:
															_snake_case													=			lyr(
															    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  encoder_attention_mask=lowerCAmelCase_			,  )[0]

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			nn.ModuleList()

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

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

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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										# Apply Film Conditional Feed Forward layer
										_snake_case													=			self.layer[-1](lowerCAmelCase_			,  lowerCAmelCase_					)

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)
										_snake_case													=			NewGELUActivation()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
										# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
										# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
										# half-precision inputs is done in fp32

										_snake_case													=			hidden_states.to(torch.floataa					).pow(2					).mean(-1			,  keepdim=lowerCAmelCase_					)
										_snake_case													=			hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

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

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase_			,  3.0					))					))





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			self.scale_bias(lowerCAmelCase_					)
										_snake_case							,       _snake_case													=			torch.chunk(lowerCAmelCase_			,  2			,  -1					)
										_snake_case													=			x * (1 + scale) + shift
										return x



