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1
2import torch
3
4from torch import nn
5
6from transformers import T5ForConditionalGeneration, T5Tokenizer
7
8from dreamscape_beam import DreamscapeBeam
9
10
11
12class QuantumComputationalUnit(nn.Module):
13
14 """
15
16 This module represents a leap in computational power, simulating quantum computing principles
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18 within a deep learning framework to process and transform data at unprecedented speeds and efficiency.
19
20 """
21
22 def __init__(self, input_dim):
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24 super(QuantumComputationalUnit, self).__init__()
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26 self.complex_transform = nn.Sequential(\
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28 nn.Linear(input_dim, 2*input_dim), nn.GELU(), nn.Linear(2*input_dim, input_dim), nn.Sigmoid())
29
30
31
32 def forward(self, x):
33
34 return self.complex_transform(x)
35
36
37
38class MultiModalDataIntegrator(nn.Module):
39
40 """
41
42 Integrates various types of data inputs, including textual, visual, and sensory data,
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44 providing a comprehensive understanding of complex environments.
45
46 """
47
48 def __init__(self):
49
50 super(MultiModalDataIntegrator, self).__init__()
51
52 self.text_processor = T5ForConditionalGeneration.from_pretrained('t5-large')
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54 self.text_tokenizer = T5Tokenizer.from_pretrained('t5-large')
55
56 # Simulations for visual and sensory data processing could be added here
57
58
59
60 def process_text(self, text):
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62 text_encoded = self.text_tokenizer(text, return_tensors='pt').input_ids
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64 text_output = self.text_processor(**text_encoded)
65
66 return text_output.logits.mean(dim=1)
67
68
69
70class GlobalCommunicationNetwork(nn.Module):
71
72 """
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74 Facilitates instant, secure communication across the framework, enabling real-time data sharing,
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76 learning, and decision-making on a global scale.
77
78 """
79
80 def __init__(self, communication_dim):
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82 super(GlobalCommunicationNetwork, self).__init__()
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84 self.global_communicator = nn.Linear(communication_dim, communication_dim)
85
86
87
88 def forward(self, data):
89
90 return torch.relu(self.global_communicator(data))
91
92
93
94class DreamscapeBeamEnhancer(nn.Module):
95
96 """
97
98 Enhances neural networks using the Dreamscape.Beam technology for advanced cognitive simulations.
99
100 """
101
102 def __init__(self):
103
104 super(DreamscapeBeamEnhancer, self).__init__()
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106 self.dreamscape_beam = DreamscapeBeam()
107
108
109
110 def forward(self, x):
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112 x = self.dreamscape_beam.process(x)
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114 return x
115
116
117
118class DijiHaxMasterFramework(nn.Module):
119
120 def __init__(self):
121
122 super(DijiHaxMasterFramework, self).__init__()
123
124 self.quantum_unit = QuantumComputationalUnit(512) # Assuming an embedding size of 512
125
126 self.data_integrator = MultiModalDataIntegrator()
127
128 self.global_network = GlobalCommunicationNetwork(512)
129
130 self.dreamscape_enhancer = DreamscapeBeamEnhancer()
131
132
133
134 def forward(self, text_input):
135
136 # Process text through the multi-modal data integrator
137
138 integrated_data = self.data_integrator.process_text(text_input)
139
140
141
142 # Enhance data processing with quantum computational power
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144 quantum_enhanced_data = self.quantum_unit(integrated_data.float())
145
146
147
148 # Apply Dreamscape.Beam enhancements to the data
149
150 dreamscape_enhanced_data = self.dreamscape_enhancer(quantum_enhanced_data)
151
152
153
154 # Leverage global communication network for distributed learning and decision making
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156 global_output = self.global_network(dreamscape_enhanced_data)
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158
159
160 return global_output
161
162
163
164def showcase_master_framework():
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166 master_framework = DijiHaxMasterFramework()
167
168 input_text = "Exploring the fusion of quantum computing and artificial intelligence with Dreamscape.Beam enhancements."
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170 output = master_framework(input_text)
171
172 print(f"DijiHax Master Framework Output with Dreamscape.Beam: {output}")
173
174
175
176if __name__ == "__main__":
177
178 showcase_master_framework()
179